Publications

These are the SPP publications

2019

A. Sheikh, N. S. Harper, J. Drefs, Y. Singer, Z. Dai, R. E. Turner, and J. Lücke: STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds. Plos computational biology, 15, e1006595, Public Library of Science, 2019. [Bibtex]

@Article{ 19-sheikh-STRFs,
author  = {Sheikh, Abdul-Saboor and Harper, Nicol S. and Drefs, Jakob
and Singer, Yosef and Dai, Zhenwen and Turner, Richard E.
and L{\"u}cke, J{\"o}rg},
journal  = {PLOS Computational Biology},
publisher  = {Public Library of Science},
title = {{STRFs} in primary auditory cortex emerge from
masking-based statistics of natural sounds},
year = {2019},
volume  = {15},
url = {https://doi.org/10.1371/journal.pcbi.1006595},
pages = {e1006595},
number  = {1},
doi = {10.1371/journal.pcbi.1006595}
}
J. Rothfuss, D. Lee, I. Clavera, Tamim Asfour, and P. Abbeel: Promp: proximal meta-policy search. In International conference on learning representations, 2019. [Bibtex]

@InProceedings{ 19-rothfuss-ProMP,
author  = {Jonas Rothfuss and Dennis Lee and Ignasi Clavera and Tamim
Asfour and Pieter Abbeel},
title = {ProMP: Proximal Meta-Policy Search},
booktitle  = {International Conference on Learning Representations},
year = {2019},
url = {https://openreview.net/forum?id=SkxXCi0qFX}
}
R. Martín-Martín, C. Eppner, and Oliver Brock: The RBO dataset of articulated objects and interactions. The international journal of robotics research, OnlineFirst, 1-6, 2019. [Bibtex]

@Article{ 19-martín-martín-RBO,
title = {The {RBO} dataset of articulated objects and
interactions},
author  = {Roberto Martín-Martín and Clemens Eppner and Oliver
Brock},
pages = {1-6},
year = {2019},
journal  = {The International Journal of Robotics Research},
volume  = {OnlineFirst},
month = {April},
pdf = {http://www.robotics.tu-berlin.de/fileadmin/fg170/Publikationen\_pdf/martin\_eppner\_19\_IJRR\_dao.pdf}
}
R. Martín-Martín and O. Brock: Coupled recursive estimation for online interactive perception of articulated objects. The international journal of robotics research, OnlineFirst, 1-33, 2019. [Bibtex]

@Article{ 19-martín-martín-Coupled,
title = {Coupled recursive estimation for online interactive
perception of articulated objects},
author  = {Roberto Martín-Martín and Oliver Brock},
pages = {1-33},
year = {2019},
journal  = {The International Journal of Robotics Research},
volume  = {OnlineFirst},
month = {May},
pdf = {http://www.robotics.tu-berlin.de/fileadmin/fg170/Publikationen\_pdf/martin-19-IJRR.pdf}
}
V. Laschos, K. Obermayer, Y. Shen, and Wilhelm Stannat: A fenchel-moreau-rockafellar type theorem on the kantorovich-wasserstein space with applications in partially observable markov decision processes. Journal of mathematical analysis applications, 477, 1133–1156, 2019. [Bibtex]

@Article{ 19-laschos-Fenchel-Moreau-Rockafellar,
author  = {Vaios Laschos and Klaus Obermayer and Yun Shen and Wilhelm
Stannat},
year = {2019},
title = {A Fenchel-Moreau-Rockafellar type theorem on the
Kantorovich-Wasserstein Space with Applications in
Partially Observable Markov Decision Processes},
journal  = {Journal of Mathematical Analysis Applications},
volume  = {477},
number  = {2},
pages = {1133--1156},
url = {https://arxiv.org/abs/1603.02882}
}
K. Hitzler, F. Meier, S. Schaal, and T. Asfour: Learning and adaptation of inverse dynamics models: a comparison. In Ieee/ras international conference on humanoid robots (humanoids), submitted, 2019. [Bibtex]

@InProceedings{ 19-hitzler-Learning,
author  = {Kevin Hitzler and Franziska Meier and Stefan Schaal and
Tamim Asfour},
title = {Learning and Adaptation of Inverse Dynamics Models: A
Comparison},
booktitle  = {IEEE/RAS International Conference on Humanoid Robots
(Humanoids)},
note = "submitted",
year = {2019}
}
F. Brandherm, T. Peters}, T. Neumann}, and T. Akrour}: Learning replanning policies with direct policy search. Ieee robotics and automation letters (ra-l), 2019. [Bibtex]

@Article{ 19-brandherm-Learning,
author  = {Florian {Brandherm} and \textbf{Jan {Peters}} and
\textbf{Gerhard {Neumann}} and \textbf{Riad {Akrour}}},
journal  = {IEEE Robotics and Automation Letters (RA-L)},
title = {Learning Replanning Policies With Direct Policy Search},
year = {2019}
}

2018

R. H. Vladimir Despotovic Oliver Walter: Machine learning techniques for semantic analysis of dysarthric speech: an experimental study. Speech communication 99 (2018) 242-251 (elsevier b.v.), 2018. [Bibtex]

@Article{ 18-vladimir-despotovic-Machine,
author  = {Vladimir Despotovic, Oliver Walter, Reinhold Haeb-Umbach},
title = {Machine learning techniques for semantic analysis of
dysarthric speech: An experimental study},
journal  = {Speech Communication 99 (2018) 242-251 (Elsevier B.V.)},
year = {2018},
month = {April},
abstract  = {We present an experimental comparison of seven
state-of-the-art machine learning algorithms for the task
of semantic analysis of spoken input, with a special
emphasis on applications for dysarthric speech. Dysarthria
is a motor speech disorder, which is characterized by poor
articulation of phonemes. In order to cater for these
noncanonical phoneme realizations, we employed an
unsupervised learning approach to estimate the acoustic
models for speech recognition, which does not require a
literal transcription of the training data. Even for the
subsequent task of semantic analysis, only weak supervision
is employed, whereby the training utterance is accompanied
by a semantic label only, rather than a literal
transcription. Results on two databases, one of them
containing dysarthric speech, are presented showing that
Markov logic networks and conditional random fields
substantially outperform other machine learning approaches.
Markov logic networks have proved to be especially robust
to recognition errors, which are caused by imprecise
articulation in dysarthric speech.},
owner = {Walter},
url = {http://nt.uni-paderborn.de/public/pubs/2018/SpeechCommunication_2018_Walter_Paper.pdf}
}
M. Toussaint, K. R. Allen, K. A. Smith, and J. B. Tenenbaum: Differentiable physics and stable modes for tool-use and manipulation planning. In Proc. of robotics: science and systems (r:ss 2018), \emph{Best Paper Award}, 2018. [Accompanying Video] [Bibtex]

@InProceedings{ 18-toussaint-Differentiable,
title = {Differentiable Physics and Stable Modes for Tool-Use and
Manipulation Planning},
author  = {Marc Toussaint and Kelsey R Allen and Kevin A Smith and
Josh B Tenenbaum},
booktitle  = {Proc{.} of Robotics: Science and Systems (R:SS 2018)},
note = {\emph{Best Paper Award}},
year = {2018},
youtube  = {-L4tCIGXKBE}
}
T. Sebastian Gomez-Gonzalez, textbf{Gerhard Neumann}, B. Schölkopf, and T. Jan Peters: Adaptation and robust learning of probabilistic movement primitives. Ieee transactions on robotics (t-ro), 2018. [Bibtex]

@Article{ 18-sebastian-gomez-gonzalez-Adaptation,
title = {Adaptation and Robust Learning of Probabilistic Movement
Primitives},
author  = {\textbf{Sebastian Gomez-Gonzalez} and \textbf{Gerhard
Neumann} and Bernhard Sch{\"o}lkopf and \textbf{Jan Peters}},
journal  = {IEEE Transactions on Robotics (T-RO)},
year = {2018}
}
J. Rothfuss, F. Ferreira, E. E. Aksoy, Y. Zhou, and T. Asfour: Deep episodic memory: encoding, recalling, and predicting episodic experiences for robot action execution. Ieee robotics and automation letters (ra-l), 3, 4007–4014, 2018. [Bibtex]

@Article{ 18-rothfuss-Deep,
author  = {Jonas Rothfuss and Fabio Ferreira and Eren Erdal Aksoy and
You Zhou and Tamim Asfour},
title = {Deep Episodic Memory: Encoding, Recalling, and Predicting
Episodic Experiences for Robot Action Execution},
pages = {4007--4014},
volume  = {3},
number  = {4},
journal  = {IEEE Robotics and Automation Letters (RA-L)},
year = {2018},
url = {https://ieeexplore.ieee.org/document/8421022}
}
T. Riad Akrour, F. Veiga, textbf{Jan Peters}, and T. Gerhard Neumann: Regularizing reinforcement learning with state abstraction. In International conference on intelligent robots and systems (iros), 2018. [Bibtex]

@InProceedings{ 18-riad-akrour-Regularizing,
author  = {\textbf{Riad Akrour} and Filipe Veiga and \textbf{Jan
Peters} and \textbf{Gerhard Neumann}},
title = {Regularizing Reinforcement Learning with State
Abstraction},
booktitle  = {International Conference on Intelligent Robots and Systems
(IROS)},
year = {2018}
}
T. Riad Akrour, A. Abdolmaleki, Hany Abdulsamad, T. Jan Peters, and textbf{Gerhard Neumann}: Model-free trajectory-based policy optimization with monotonic improvement. Journal of machine learning resource (jmlr), 2018. [Bibtex]

@Article{ 18-riad-akrour-Model-Free,
title = {Model-Free Trajectory-based Policy Optimization with
Monotonic Improvement},
author  = {\textbf{Riad Akrour} and Abbas Abdolmaleki and Hany
Abdulsamad and \textbf{Jan Peters} and \textbf{Gerhard
Neumann}},
journal  = {Journal of Machine Learning Resource (JMLR)},
year = {2018}
}
M. Plappert, C. Mandery, and T. Asfour: Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks. Robotics and autonomous systems, 109, 13–26, 2018. [Bibtex]

@Article{ 18-plappert-Learning,
author  = {Matthias Plappert and Christian Mandery and Tamim Asfour},
title = {Learning a Bidirectional Mapping Between Human Whole-Body
Motion and Natural Language Using Deep Recurrent Neural
Networks},
pages = {13--26},
volume  = {109},
journal  = {Robotics and Autonomous Systems},
year = {2018},
url = {https://www.sciencedirect.com/science/article/pii/S0921889017306280?via%3Dihub}
}
R. Pinsler, T. Akrour}, Osa, T. Peters}, and Neumann}: Sample and feedback efficient hierarchical reinforcement learning from human preferences. In International conference on robotics and automation (icra), 2018. [Bibtex]

@InProceedings{ 18-pinsler-Sample,
author  = {Robert {Pinsler} and \textbf{Riad {Akrour}} and Takayuki
{Osa} and \textbf{Jan {Peters}} and \textbf{Gerhard
{Neumann}}},
booktitle  = {International Conference on Robotics and Automation
(ICRA)},
title = {Sample and Feedback Efficient Hierarchical Reinforcement
Learning from Human Preferences},
year = {2018}
}
S. Ottenhaus, L. Kaul, N. Vahrenkamp, and T. Asfour: Active tactile exploration based on cost-aware information gain maximization. International journal of humanoid robotics, 15, 1–21, 2018. [Bibtex]

@Article{ 18-ottenhaus-Active,
author  = {Simon Ottenhaus and Lukas Kaul and Nikolaus Vahrenkamp and
Tamim Asfour},
title = {Active Tactile Exploration Based on Cost-Aware Information
Gain Maximization},
pages = {1--21},
volume  = {15},
number  = {1},
journal  = {International Journal of Humanoid Robotics},
year = {2018},
url = {https://www.worldscientific.com/doi/10.1142/S0219843618500159}
}
T. Monk, C. Savin, and J. Lücke: Optimal neural inference of stimulus intensities. Scientific reports, 10038, 2018. [Bibtex]

@Article{ 18-monk-Optimal,
author  = {Monk, Travis and Savin, Cristina and L{\"u}cke, J{\"o}rg},
title = {Optimal Neural Inference of Stimulus Intensities},
journal  = {Scientific Reports},
year = {2018},
number  = {8},
pages = {10038},
url = {https://www.nature.com/articles/s41598-018-28184-5}
}
R. Martín-Martín: Leveraging problem structure in interactive perception for robot manipulation of constrained mechanisms. 2018. [Bibtex]

@PhDThesis{ 18-martin-martin-Leveraging,
title = {Leveraging problem structure in interactive perception for
robot manipulation of constrained mechanisms},
author  = {Mart{\'i}n-Mart{\'i}n, Roberto},
school  = {Technische Universit{\"a}t Berlin},
year = {2018},
pdf = {https://depositonce.tu-berlin.de/handle/11303/7512}
}
Q. Li, L. Natale, R. Haschke, Andrea Cherubini, A. Ho, and H. Ritter: Tactile sensing for manipulation. International journal of humanoid robotics, 15, 2018. [Bibtex]

@Article{ 18-li-Tactile,
author  = {Qiang Li and Lorenzo Natale and Robert Haschke and Andrea
Cherubini and Anh-Van Ho and Helge Ritter},
year = {2018},
month = {Feb},
title = {Tactile Sensing for Manipulation},
volume  = {15},
journal  = {International Journal of Humanoid Robotics},
url = {https://www.worldscientific.com/doi/abs/10.1142/S0219843618020012}
}
Q. Li, A. Uckermann, R. Haschke, and Helge Ritter: Estimating an articulated tool’s kinematics via visuo-tactile based robotic interactive manipulation. In Ieee/rsj international conference on intelligent robots and systems (iros), 6938-6944, 2018. [Bibtex]

@InProceedings{ 18-li-Estimating,
author  = {Qiang Li and Andre Uckermann and Robert Haschke and Helge
Ritter},
year = {2018},
month = {Oct},
pages = {6938-6944},
title = {Estimating an Articulated Tool's Kinematics via
Visuo-Tactile Based Robotic Interactive Manipulation},
booktitle  = {IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS)},
url = {https://ieeexplore.ieee.org/document/8594295}
}
E. Ilg, Ö. Çiçek, S. Galesso, A. Klein, O. Makansi, F. Hutter, and T. Brox: Uncertainty estimates and multi-hypotheses networks for optical flow. In Computer vision – eccv 2018, 677–693, Springer International Publishing, 2018. [Bibtex]

@InProceedings{ 18-ilg-Uncertainty,
author  = {E. Ilg and {\"O}. {\c{C}}i{\c{c}}ek and S. Galesso and A.
Klein and O. Makansi and F. Hutter and T. Brox},
editor  = {V. Ferrari and M. Hebert and C. Sminchisescu and Y.
Weiss},
title = {Uncertainty Estimates and Multi-hypotheses Networks for
Optical Flow},
booktitle  = {Computer Vision -- ECCV 2018},
year = {2018},
publisher  = {Springer International Publishing},
pages = {677--693},
url = {http://openaccess.thecvf.com/content_ECCV_2018/papers/Eddy_Ilg_Uncertainty_Estimates_and_ECCV_2018_paper.pdf}
}
T. Glarner, P. Hanebrink, J. Ebbers, and R. Haeb-Umbach: Full bayesian hidden markov model variational autoencoder for acoustic unit discovery. In Interspeech 2018, 2018. [Bibtex]

@InProceedings{ 18-glarner-Full,
author  = {Thomas Glarner and Patrick Hanebrink and Janek Ebbers and
Reinhold Haeb-Umbach},
title = {Full Bayesian Hidden Markov Model Variational Autoencoder
for Acoustic Unit Discovery},
booktitle  = {INTERSPEECH 2018},
year = {2018},
address  = {Hyderabad, India},
month = {September},
abstract  = {The invention of the Variational Autoencoder enables the
application of Neural Networks to a wide range of tasks in
unsupervised learning, including the field of Acoustic Unit
Discovery (AUD). The recently proposed Hidden Markov Model
Variational Autoencoder (HMMVAE) allows a joint training of
a neural network based feature extractor and a structured
prior for the latent space given by a Hidden Markov Model.
It has been shown that the HMMVAE significantly outperforms
pure GMM-HMM based systems on the AUD task. However, the
HMMVAE cannot autonomously infer the number of acoustic
units and thus relies on the GMM-HMM system for
initialization. This paper introduces the Bayesian Hidden
Markov Model Variational Autoencoder (BHMMVAE) which solves
these issues by embedding the HMMVAE in a Bayesian
framework with a Dirichlet Process Prior for the
distribution of the acoustic units, and diagonal or
full-covariance Gaussians as emission distributions.
Experiments on TIMIT and Xitsonga show that the BHMMVAE is
able to autonomously infer a reasonable number of acoustic
units, can be initialized without supervision by a GMM-HMM
system, achieves computationally efficient stochastic
variational inference by using natural gradient descent,
and, additionally, improves the AUD performance over the
HMMVAE.},
comment  = {[slides]},
owner = {Glarner},
url = {https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Glarner_Paper.pdf}
}
J. Gao, Y. Zhou, and T. Asfour: Projected force-admittance control for compliant bimanual tasks. In Ieee/ras international conference on humanoid robots (humanoids), 607–613, 2018. [Bibtex]

@InProceedings{ 18-gao-Projected,
author  = {Jianfeng Gao and You Zhou and Tamim Asfour},
title = {Projected Force-Admittance Control for Compliant Bimanual
Tasks},
booktitle  = {IEEE/RAS International Conference on Humanoid Robots
(Humanoids)},
pages = {607--613},
year = {2018},
url = {https://ieeexplore.ieee.org/document/8624916}
}
D. Forster, A. Sheikh, and J. Lücke: Neural simpletrons: learning in the limit of few labels with directed generative networks. Neural computation, 2113–2174, MIT Press, 2018. [Bibtex]

@Article{ 18-forster-Neural,
title = {Neural Simpletrons: Learning in the Limit of Few Labels
with Directed Generative Networks},
author  = {Dennis Forster and Abdul-Saboor Sheikh and J\"org L\"ucke},
journal  = {Neural Computation},
number  = {30},
pages = {2113--2174},
year = {2018},
publisher  = {MIT Press},
url = {https://www.mitpressjournals.org/doi/abs/10.1162/neco_a_01100}
}
D. Forster and J. Lücke: Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and k-means. In Aistats, 124-132, 2018. [Bibtex]

@InProceedings{ 18-forster-Can,
author  = {Forster, Dennis and L{\"u}cke, J{\"o}rg},
title = {Can clustering scale sublinearly with its clusters? {A}
variational {EM} acceleration of {GMM}s and k-means},
year = {2018},
pages = {124-132},
booktitle  = {AISTATS},
url = {http://proceedings.mlr.press/v84/forster18a.html}
}
S. Falkner, A. Klein, and F. Hutter: BOHB: robust and efficient hyperparameter optimization at scale. In Proceedings of the 35th international conference on machine learning, 80, 1437–1446, PMLR, 2018. [Bibtex]

@InProceedings{ 18-falkner-BOHB,
title = {{BOHB}: Robust and Efficient Hyperparameter Optimization
at Scale},
author  = {S. Falkner and A. Klein and F. Hutter},
booktitle  = {Proceedings of the 35th International Conference on
Machine Learning},
pages = {1437--1446},
year = {2018},
editor  = {J. Dy and A. Krause},
volume  = {80},
publisher  = {PMLR},
url = {http://proceedings.mlr.press/v80/falkner18a/falkner18a.pdf}
}
C. Eppner, R. R. Martín-Martín, and O. Brock: Physics-based selection of informative actions for interactive perception. In Ieee international conference on robotics and automation (icra), 7427-7432, 2018. [Bibtex]

@InProceedings{ 18-eppner-Physics-Based,
title = {Physics-Based Selection of Informative Actions for
Interactive Perception},
author  = {Clemens Eppner and Roberto Roberto {Mart{\'i}n-Mart{\'i}n}
and Oliver Brock},
booktitle  = {IEEE International Conference on Robotics and Automation
(ICRA)},
pages = {7427-7432},
year = {2018},
pdf = {http://www.robotics.tu-berlin.de/fileadmin/fg170/Publikationen\_pdf/eppnermartin\_18\_icra.pdf},
projectname  = {Interactive Perception}
}
P. Englert and M. Toussaint: Learning manipulation skills from a single demonstration. The international journal of robotics research, 37, 137-154, 2018. [Accompanying Video] [Bibtex]

@Article{ 18-englert-Learning,
author  = {Peter Englert and Marc Toussaint},
title = {Learning manipulation skills from a single demonstration},
journal  = {The International Journal of Robotics Research},
volume  = {37},
number  = {1},
pages = {137-154},
year = {2018},
youtube  = {sG01B_GcTJQ},
pdf = {http://ipvs.informatik.uni-stuttgart.de/mlr/papers/18-englert-IJRR.pdf}
}
P. Englert and M. Toussaint: Kinematic morphing networks for manipulation skill transfer. In Proc. of the int. conf. on intelligent robots and systems (iros 2018), 2018. [Accompanying Video] [Bibtex]

@InProceedings{ 18-englert-Kinematic,
title = {Kinematic Morphing Networks for Manipulation Skill
Transfer},
author  = {Peter Englert and Marc Toussaint},
booktitle  = iros # { (IROS 2018)},
year = {2018},
youtube  = {hI1BC0G0oD4},
pdf = {http://ipvs.informatik.uni-stuttgart.de/mlr/papers/18-englert-IROS.pdf}
}

2017

Y. Zhou and T. Asfour: Task-oriented generalization of dynamic movement primitive. In Ieee/rsj international conference on intelligent robots and systems (iros), 3202-3209, 2017. [Bibtex]

@InProceedings{ 17-zhou-Task-Oriented,
author  = {You Zhou and Tamim Asfour},
title = {Task-Oriented Generalization of Dynamic Movement
Primitive},
booktitle  = {IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS)},
pages = {3202-3209},
year = {2017},
url = {https://ieeexplore.ieee.org/document/8206153}
}
J. Zhang, T. Jost Tobias Springenberg, T. Joschka Boedecker, and W. Burgard: Deep reinforcement learning with successor features for navigation across similar environments. In Ieee/rsj international conference on intelligent robots and systems (iros), 2017. [Bibtex]

@InProceedings{ 17-zhang-Deep,
title = {Deep reinforcement learning with successor features for
navigation across similar environments},
author  = {Jingwei Zhang and \textbf{Jost Tobias Springenberg} and
\textbf{Joschka Boedecker} and Wolfram Burgard},
booktitle  = {IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS)},
year = {2017}
}
V. Tangkaratt, H. van Hoof, textbf{Simone Parisi}, G. Neumann, T. Jan Peters, and M. Sugiyama: Policy search with High-Dimensional context variables. In Proceedings of the conference on artificial intelligence (aaai), 2017. [Bibtex]

@InProceedings{ 17-tangkaratt-Policy,
title = {Policy Search with {High-Dimensional} Context Variables},
author  = {Voot Tangkaratt and Herke van Hoof and \textbf{Simone
Parisi} and Gerhard Neumann and \textbf{Jan Peters} and
Masashi Sugiyama},
booktitle  = {Proceedings of the Conference on Artificial Intelligence
(AAAI)},
year = {2017}
}
T. Simone Parisi, M. Pirotta, and textbf{Jan Peters}: Manifold-based Multi-objective policy search with sample reuse. Neurocomputing, 263, 3–14, 2017. [Bibtex]

@Article{ 17-simone-parisi-Manifold-based,
title = {{Manifold-based} {Multi-objective} Policy Search with
Sample Reuse},
author  = {\textbf{Simone Parisi} and Matteo Pirotta and \textbf{Jan
Peters}},
journal  = {Neurocomputing},
year = {2017},
pages = {3--14},
volume  = {263}
}
T. Simone Parisi, S. Ramstedt, and textbf{Jan Peters}: Goal-Driven dimensionality reduction for reinforcement learning. In Proceedings of the international conference on intelligent robots and systems (iros), 2017. [Bibtex]

@InProceedings{ 17-simone-parisi-Goal-Driven,
title = {{Goal-Driven} Dimensionality Reduction for Reinforcement
Learning},
author  = {\textbf{Simone Parisi} and Simon Ramstedt and \textbf{Jan
Peters}},
booktitle  = {Proceedings of the International Conference on Intelligent
Robots and Systems (IROS)},
year = {2017}
}
J. A. Shelton, J. Gasthaus, Z. Dai, J. Lücke, and A. Gretton: Gp-select: accelerating em using adaptive subspace preselection. Neural computation, 29, 2177–2202, MIT Press, 2017. [Bibtex]

@Article{ 17-shelton-GP-select,
title = {GP-select: Accelerating EM using adaptive subspace
preselection},
author  = {Shelton, Jacquelyn A and Gasthaus, Jan and Dai, Zhenwen
and L{\"u}cke, J{\"o}rg and Gretton, Arthur},
journal  = {Neural Computation},
year = {2017},
pages = {2177--2202},
volume  = {29},
number  = {8},
publisher  = {MIT Press},
url = {http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00982}
}
T. Riad Akrour, D. Sorokin, textbf{Jan Peters}, and T. Gerhard Neumann: Local bayesian optimization of motor skills. In International conference on machine learning (icml), 2017. [Bibtex]

@InProceedings{ 17-riad-akrour-Local,
author  = "\textbf{Riad Akrour} and Dmitry Sorokin and \textbf{Jan
Peters} and \textbf{Gerhard Neumann}",
year = "2017",
title = "Local Bayesian Optimization of Motor Skills",
booktitle  = "International Conference on Machine Learning (ICML)"
}
R. Martín-Martín and O. Brock: Cross-modal interpretation of multi-modal sensor streams in interactive perception based on coupled recursion. In Ieee/rsj international conference on intelligent robots and systems (iros), 3289-3295, 2017. [Bibtex]

@InProceedings{ 17-martin-martin-Cross-Modal,
title = {Cross-Modal Interpretation of Multi-Modal Sensor Streams
in Interactive Perception Based on Coupled Recursion},
author  = {Roberto {Mart{\'i}n-Mart{\'i}n} and Oliver Brock},
booktitle  = {IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS)},
pages = {3289-3295},
year = {2017},
location  = {Vancouver, Canada},
editor  = {IEEE},
organization  = {IEEE},
pdf = {http://www.robotics.tu-berlin.de/fileadmin/fg170/Publikationen\_pdf/martinmartin\_17\_iros.pdf}
}
R. Martín-Martín and O. Brock: Building kinematic and dynamic models of articulated objects with multi-modal interactive perception. In Aaai symposium on interactive multi-sensory object perception for embodied agents, 2017. [Bibtex]

@InProceedings{ 17-martin-martin-Building,
title = {Building Kinematic and Dynamic Models of Articulated
Objects with Multi-Modal Interactive Perception},
author  = {Roberto {Mart{\'i}n-Mart{\'i}n} and Oliver Brock},
booktitle  = {AAAI Symposium on Interactive Multi-Sensory Object
Perception for Embodied Agents},
year = {2017},
pdf = {http://www.redaktion.tu-berlin.de/fileadmin/fg170/Publikationen\_pdf/martinmartin\_17\_aaais.pdf},
url2 = {https://aaai.org/ocs/index.php/SSS/SSS17/paper/download/15318/14590}
}
A. Klein, S. Falkner, N. Mansur, and F. Hutter: RoBO: a flexible and robust Bayesian optimization framework in Python. In NIPS workshop on Bayesian optimization (BayesOpt’17), 2017. [Bibtex]

@InProceedings{ 17-klein-RoBO,
author  = {A. Klein and S. Falkner and N. Mansur and F. Hutter},
title = {{RoBO}: A Flexible and Robust {B}ayesian Optimization
Framework in {P}ython},
booktitle  = {{NIPS} workshop on {B}ayesian Optimization
({BayesOpt}'17)},
year = {2017},
url = {https://bayesopt.github.io/papers/2017/22.pdf}
}
A. Klein, S. Falkner, J. Springenberg, and F. Hutter: Learning curve prediction with Bayesian neural networks. In Proceedings of the international conference on learning representations (iclr’17), Published online: \url{iclr.cc}, 2017. [Bibtex]

@InProceedings{ 17-klein-Learning,
author  = {A. Klein and S. Falkner and J. Springenberg and F.
Hutter},
title = {Learning Curve Prediction with {Bayesian} Neural
Networks},
booktitle  = {Proceedings of the International Conference on Learning
Representations (ICLR'17)},
year = {2017},
note = {Published online: \url{iclr.cc}},
url = {https://openreview.net/pdf?id=S11KBYclx}
}
A. Klein, S. Falkner, S. Bartels, P. Hennig, and F. Hutter: Fast Bayesian hyperparameter optimization on large datasets. In Electronic journal of statistics, 11, 4945–4968, 2017. [Bibtex]

@InProceedings{ 17-klein-Fast,
author  = {A. Klein and S. Falkner and S. Bartels and P. Hennig and
F. Hutter},
title = {Fast {B}ayesian hyperparameter optimization on large
datasets},
booktitle  = {Electronic Journal of Statistics},
year = {2017},
volume  = {11},
pages = {4945–4968},
url = {https://projecteuclid.org/download/pdfview_1/euclid.ejs/1513306864}
}
M. S. Kanwal, J. A. Grochow, and N. Ay: Comparing information-theoretic measures of complexity in Boltzmann machines. Entropy, 19, 310, 2017. [Bibtex]

@Article{ 17-kanwal-Comparing-information-theoretic-measures-of-complexity-in-Boltzmann-machines,
author  = {Maxinder S. Kanwal and Joshua A. Grochow and Nihat Ay},
title = {{Comparing information-theoretic measures of complexity in
Boltzmann machines}},
doi = {10.3390/e19070310},
journal  = {Entropy},
pages = {310},
year = {2017},
volume  = {19},
number  = {7},
issn = {1099-4300}
}
R. Holca-Lamarre, J. Lücke, and K. Obermayer: Models of acetylcholine and dopamine signals differentially improve neural representations. Frontiers in computational neuroscience, 11, 54, Frontiers, 2017. [Bibtex]

@Article{ 17-holca-lamarre-Models,
title = {Models of Acetylcholine and Dopamine Signals
Differentially Improve Neural Representations},
author  = {Holca-Lamarre, Rapha{\"e}l and L{\"u}cke, J{\"o}rg and
Obermayer, Klaus},
journal  = {Frontiers in Computational Neuroscience},
year = {2017},
volume  = {11},
pages = {54},
publisher  = {Frontiers},
doi = {10.3389/fncom.2017.00054},
url = {http://journal.frontiersin.org/article/10.3389/fncom.2017.00054/full}
}
S. Höfer: On decomposability in robot reinforcement learning. In Ph.d.thesis, 2017. [Bibtex]

@PhDThesis{ 17-hofer-On,
title = {On decomposability in robot reinforcement learning},
author  = {Sebastian H{\"o}fer},
booktitle  = {Ph.D.Thesis},
year = {2017},
address  = {Berlin, Germany},
school  = {Technische Universit{\"a}t Berlin},
pdf = {http://dx.doi.org/10.14279/depositonce-6054},
projectname  = {ballcatching}
}
T. Glarner, B. Boenninghoff, O. Walter, and R. Haeb-Umbach: Leveraging text data for word segmentation for underresourced languages. In Interspeech 2017, stockholm, schweden, 2017. [Bibtex]

@InProceedings{ 17-glarner-Leveraging,
author  = {Glarner, Thomas and Boenninghoff, Benedikt and Walter,
Oliver and Haeb-Umbach, Reinhold},
title = {Leveraging Text Data for Word Segmentation for
Underresourced Languages},
booktitle  = {INTERSPEECH 2017, Stockholm, Schweden},
year = {2017},
month = {August},
abstract  = {In this contribution we show how to exploit text data to
support word discovery from audio input in an
underresourced target language. Given audio, of which a
certain amount is transcribed at the word level, and
additional unrelated text data, the approach is able to
learn a probabilistic mapping from acoustic units to
characters and utilize it to segment the audio data into
words without the need of a pronunciation dictionary. This
is achieved by three components: an unsupervised acoustic
unit discovery system, a supervisedly trained acoustic
unit-to-grapheme converter, and a word discovery system,
which is initialized with a language model trained on the
text data. Experiments for multiple setups show that the
initialization of the language model with text data
improves the word segementation performance by a large
margin.},
comment  = {[poster]},
owner = {Glarner},
url = {https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Glarner_paper.pdf}
}
K. Ghazi-Zahedi, C. Langer, and N. Ay: Morphological computation\,:\,synergy of body and brain. Entropy, 19, 456, 2017. [Bibtex]

@Article{ 17-ghazi-zahedi-Morphological-computation-synergy-of-body-and-brain,
author  = {Keyan Ghazi-Zahedi and Carlotta Langer and Nihat Ay},
title = {{Morphological computation\,:\,synergy of body and brain}},
doi = {10.3390/e19090456},
journal  = {Entropy},
pages = {456},
year = {2017},
volume  = {19},
number  = {9},
issn = {1099-4300}
}
D. Forster and J. Lücke: Truncated variational EM for semi-supervised Neural Simpletrons. In International joint conference on neural networks (ijcnn), 3769-3776, 2017. [Bibtex]

@InProceedings{ 17-forster-Truncated,
author  = {Forster, Dennis and L{\"u}cke, J{\"o}rg},
booktitle  = {International Joint Conference on Neural Networks
(IJCNN)},
title = {Truncated Variational {EM} for Semi-Supervised {N}eural
{S}impletrons},
year = {2017},
pages = {3769-3776},
url = {http://ieeexplore.ieee.org/document/7966331/}
}
T. P. {Felix End textbf{Riad Akrour} and T. Gerhard Neumann}: Layered Direct Policy Search for Learning Hierarchical Skills. In International conference on robotics and automation (icra), 2017. [Bibtex]

@InProceedings{ 17-felix-end-Layered-Direct-Policy-Search-for-Learning-Hierarchical-Skills,
title = {{Layered Direct Policy Search for Learning Hierarchical
Skills}},
author  = {{Felix End, \textbf{Riad Akrour}, \textbf{Jan Peters} and
\textbf{Gerhard Neumann}}},
booktitle  = {International Conference on Robotics and Automation
(ICRA)},
year = {2017}
}
Exarchakis G. and J. Lücke: Discrete sparse coding. Neural computation, 29, 2979–3013, MIT Press, 2017. [Bibtex]

@Article{ 17-exarchakis-Discrete,
title = {Discrete Sparse Coding},
author  = {Exarchakis, G., and L\"ucke, J.},
journal  = {Neural Computation},
year = {2017},
volume  = {29},
pages = {2979--3013},
publisher  = {MIT Press},
url = {https://www.mitpressjournals.org/doi/full/10.1162/neco_a_01015}
}
C. Eppner, R. Martín-Martín, and Oliver Brock: Physics-based selection of actions that maximize motion for interactive perception. In Rss workshop: revisiting contact – turning a problem into a solution, 2017. [Bibtex]

@Misc{ 17-eppner-Physics-Based,
title = {Physics-Based Selection of Actions That Maximize Motion
for Interactive Perception},
author  = {Clemens Eppner and Roberto Martín-Martín and Oliver
Brock},
booktitle  = {RSS workshop: Revisiting Contact - Turning a problem into
a solution},
year = {2017},
pdf = {http://www.robotics.tu-berlin.de/fileadmin/fg170/Publikationen\_pdf/EppnerMartinMartin17\_RSS\_WS.pdf}
}
P. Englert, N. A. Vien, and M. Toussaint: Inverse KKT: learning cost functions of manipulation tasks from demonstrations. The international journal of robotics research, 36, 1474-1488, 2017. [Bibtex]

@Article{ 17-englert-Inverse,
author  = {Peter Englert and Ngo Anh Vien and Marc Toussaint},
title = {Inverse {KKT}: Learning cost functions of manipulation
tasks from demonstrations},
journal  = {The International Journal of Robotics Research},
volume  = {36},
number  = {13-14},
pages = {1474-1488},
year = {2017}
}
P. Englert: Advancing manipulation skill learning towards sample-efficiency and generalization. In Ph.d.thesis, 2017. [Bibtex]

@PhDThesis{ 17-englert-Advancing,
title = {Advancing Manipulation Skill Learning Towards
Sample-Efficiency and Generalization},
author  = {Peter Englert},
booktitle  = {Ph.D.Thesis},
school  = {University of Stuttgart},
year = {2017}
}
J. Ebbers, J. Heymann, L. Drude, T. Glarner, R. Haeb-Umbach, and B. Raj: Hidden markov model variational autoencoder for acoustic unit discovery. In Interspeech 2017, stockholm, schweden, 2017. [Bibtex]

@InProceedings{ 17-ebbers-Hidden,
author  = {Ebbers, Janek and Heymann, Jahn and Drude, Lukas and
Glarner, Thomas and Haeb-Umbach, Reinhold and Raj,
Bhiksha},
title = {Hidden Markov Model Variational Autoencoder for Acoustic
Unit Discovery},
booktitle  = {INTERSPEECH 2017, Stockholm, Schweden},
year = {2017},
month = {August},
abstract  = {Variational Autoencoders (VAEs) have been shown to provide
efficient neural-network-based approximate Bayesian
inference for observation models for which exact inference
is intractable. Its extension, the so-called Structured VAE
(SVAE) allows inference in the presence of both discrete
and continuous latent variables. Inspired by this
extension, we developed a VAE with Hidden Markov Models
(HMMs) as latent models. We applied the resulting HMM-VAE
to the task of acoustic unit discovery in a zero resource
scenario. Starting from an initial model based on
variational inference in an HMM with Gaussian Mixture Model
(GMM) emission probabilities, the accuracy of the acoustic
unit discovery could be significantly improved by the
HMM-VAE. In doing so we were able to demonstrate for an
unsupervised learning task what is well-known in the
supervised learning case: Neural networks provide superior
modeling power compared to GMMs.},
comment  = {[poster]
[slides]},
owner = {Ebbers},
url = {https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Ebbers_paper.pdf}
}
D. Driess, P. Englert, and M. Toussaint: Constrained bayesian optimization of combined interaction force/task space controllers for manipulations. In Proc. of the ieee int. conf. on robotics and automation (icra 2017), 2017. [Accompanying Video] [Bibtex]

@InProceedings{ 17-driess-Constrained,
title = {Constrained Bayesian Optimization of Combined Interaction
Force/Task Space Controllers for Manipulations},
author  = {Danny Driess and Peter Englert and Marc Toussaint},
booktitle  = icra # { (ICRA 2017)},
youtube  = {CudjsbB7sfM},
year = {2017},
pdf = {http://ipvs.informatik.uni-stuttgart.de/mlr/papers/17-driess-ICRA.pdf}
}
S. Drgas, T. Virtanen, J. Lücke, and A. Hurmalainen: Binary non-negative matrix deconvolution for audio dictionary learning. Ieee/acm transactions on audio, speech, and language processing, 25, 1644-1656, IEEE, 2017. [Bibtex]

@Article{ 17-drgas-Binary,
title = {Binary non-negative matrix deconvolution for audio
dictionary learning},
author  = {Drgas, Szymon and Virtanen, Tuomas and L{\"u}cke, J{\"o}rg
and Hurmalainen, Antti},
journal  = {IEEE/ACM Transactions on Audio, Speech, and Language
Processing},
year = {2017},
pages = {1644-1656},
volume  = {25},
publisher  = {IEEE},
url = {http://ieeexplore.ieee.org/document/7935444/}
}
W. Böhmer: Representation and generalization in autonomous reinforcement learning. URI: \url{http://dx.doi.org/10.14279/depositonce-5715}, 2017. [Bibtex]

@PhDThesis{ 17-bohmer-Representation,
author  = {Wendelin B\"ohmer},
title = {Representation and generalization in autonomous
reinforcement learning},
school  = {Technische Universit\"at Berlin},
year = 2017,
note = {URI: \url{http://dx.doi.org/10.14279/depositonce-5715}}
}
J. Bohg, K. Hausman, B. Sankaran, O. Brock, D. Kragic, and S. S. G. Sukhatme: Interactive perception: leveraging action in perception and perception in action. Ieee transactions on robotics, 33, 1273-1291, 2017. [Bibtex]

@Article{ 17-bohg-Interactive,
title = {Interactive Perception: Leveraging Action in Perception
and Perception in Action},
author  = {Jeannette Bohg and Karol Hausman and Bharath Sankaran and
Oliver Brock and Danica Kragic and Stefan Schaaland Gaurav
S. Sukhatme},
pages = {1273-1291},
year = {2017},
journal  = {IEEE Transactions on Robotics},
volume  = {33},
number  = {6},
month = {december},
pdf = {http://www.robotics.tu-berlin.de/fileadmin/fg170/Publikationen\_pdf/Jannette\_2017\_TransactionsOnRobotics.pdf},
projectname  = {Interactive Perception}
}
B. Bischl, G. Casalicchio, M. Feurer, F. Hutter, M. Lang, R. Mantovani, J. van Rijn, and J. Vanschoren: Openml benchmarking suites and the openml100. Arxiv:1708.03731, 2017. [Bibtex]

@Article{ 17-bischl-OpenML,
title = {OpenML benchmarking suites and the OpenML100},
author  = {B. Bischl and G. Casalicchio and M. Feurer and F. Hutter
and M. Lang and R. Mantovani and J. van Rijn and J.
Vanschoren},
journal  = {arXiv:1708.03731},
year = {2017},
url = {https://arxiv.org/pdf/1708.03731.pdf}
}
M. Baum, M. Bernstein, Martín-Martín, S. Höfer, Johannes Kulick, M. Toussaint, A. Kacelnik, and Oliver Brock: Opening a lockbox through physical exploration. In Proceedings of the ieee international conference on humanoid robots (humanoids), 2017. [Bibtex]

@InProceedings{ 17-baum-Opening,
title = {Opening a Lockbox through Physical Exploration},
author  = {Manuel Baum and Matthew Bernstein and Roberto
{Mart{\'i}n-Mart{\'i}n} and Sebastian Höfer and Johannes
Kulick and Marc Toussaint and Alex Kacelnik and Oliver
Brock},
booktitle  = {Proceedings of the IEEE International Conference on
Humanoid Robots (Humanoids)},
year = {2017},
pdf = {http://www.robotics.tu-berlin.de/fileadmin/fg170/Publikationen\_pdf/baum\_17\_humanoids.pdf},
url2 = {http://ieeexplore.ieee.org/document/8246913/},
projectname  = {Interactive Perception}
}
M. Baum and O. Brock: Achieving robustness by optimizing failure behavior. In Proceedings of the ieee international conference on robotics and automation (icra), 5806-5811, 2017. [Bibtex]

@InProceedings{ 17-baum-Achieving,
title = {Achieving Robustness by Optimizing Failure Behavior},
author  = {Manuel Baum and Oliver Brock},
booktitle  = {Proceedings of the IEEE International Conference on
Robotics and Automation (ICRA)},
pages = {5806-5811},
year = {2017},
pdf = {http://www.robotics.tu-berlin.de/fileadmin/fg170/Publikationen\_pdf/baum\_icra2017.pdf},
projectname  = {Interactive Perception}
}
T. {Alexander Gabriel textbf{Riad Akrour} and T. Gerhard Neumann}: Empowered skills. In International conference on robotics and automation (icra), 2017. [Bibtex]

@InProceedings{ 17-alexander-gabriel-Empowered-skills,
title = {{Empowered skills}},
author  = {{Alexander Gabriel, \textbf{Riad Akrour}, \textbf{Jan
Peters} and \textbf{Gerhard Neumann}}},
booktitle  = {International Conference on Robotics and Automation
(ICRA)},
year = {2017}
}

2016

O. Walter and R. Haeb-Umbach: Unsupervised word discovery from speech using bayesian hierarchical models. In 38th german conference on pattern recognition (gcpr), 2016. [Bibtex]

@InProceedings{ 16-walter-Unsupervised,
author  = {Walter, Oliver and Haeb-Umbach, Reinhold},
title = {Unsupervised Word Discovery from Speech using Bayesian
Hierarchical Models},
booktitle  = {38th German Conference on Pattern Recognition (GCPR)},
year = {2016},
month = {sep},
abstract  = {In this paper we demonstrate an algorithm to learn words
from speech using non-parametric Bayesian hierarchical
models in an unsupervised setting. We exploit the
assumption of a hierarchical structure of speech, namely
the formation of spoken words as a sequence of phonemes. We
employ the Nested Hierarchical Pitman-Yor Language Model,
which allows an a priori unknown and possibly unlimited
number of words. We assume the n-gram probabilities of
words, the m-gram probabilities of phoneme sequences in
words and the phoneme sequences of the words themselves as
latent variables to be learned. We evaluate the algorithm
on a cross language task using an existing speech
recognizer trained on English speech to decode speech in
the Xitsonga language supplied for the 2015 ZeroSpeech
challenge. We apply the learning algorithm on the resulting
phoneme graphs and achieve the highest token precision and
F score compared to present systems.},
comment  = {[Presentation]},
url = {https://groups.uni-paderborn.de/nt/pubs/2016/WaHa16.pdf}
}
N. A. Vien, P. Englert, and M. Toussaint: Policy search in reproducing kernel hilbert space. In Proc. of the int. joint conf. on artificial intelligence (ijcai 2016), 2016. [Bibtex]

@InProceedings{ 16-vien-Policy,
title = {Policy Search in Reproducing Kernel Hilbert Space},
author  = {Ngo Anh Vien and Peter Englert and Marc Toussaint},
booktitle  = ijcai # { (IJCAI 2016)},
year = {2016},
pdf = {http://ipvs.informatik.uni-stuttgart.de/mlr/papers/16-vien-IJCAI.pdf}
}
M. Toussaint: A tutorial on Newton methods for constrained trajectory optimization and relations to SLAM, Gaussian Process smoothing, optimal control, and probabilistic inference. In Geometric and numerical foundations of movements, Springer, 2016. [Bibtex]

@InCollection{ 16-toussaint-tutorial,
author  = {Marc Toussaint},
booktitle  = {Geometric and Numerical Foundations of Movements},
date-added  = {2017-10-24 14:28:20 +0000},
date-modified  = {2017-10-24 14:28:20 +0000},
editor  = {Jean-Paul Laumond},
key = {Newton},
publisher  = {Springer},
title = {A tutorial on {N}ewton methods for constrained trajectory
optimization and relations to {SLAM}, {G}aussian {P}rocess
smoothing, optimal control, and probabilistic inference},
year = {2016},
pdf = {http://ipvs.informatik.uni-stuttgart.de/mlr/papers/16-toussaint-Newton.pdf}
}
J. Springenberg, A. Klein, S. Falkner, and F. Hutter: Bayesian optimization with robust Bayesian neural networks. In Proceedings of the 30th international conference on advances in neural information processing systems, 4134–4142, 2016. [Bibtex]

@InProceedings{ 16-springenberg-Bayesian,
title = {Bayesian Optimization with Robust {B}ayesian Neural
Networks},
author  = {J. Springenberg and A. Klein and S. Falkner and F.
Hutter},
editor  = {D. Lee and M. Sugiyama and U. von Luxburg and I. Guyon and
R. Garnett},
booktitle  = {Proceedings of the 30th International Conference on
Advances in Neural Information Processing Systems},
year = {2016},
pages = {4134--4142},
url = {https://papers.nips.cc/paper/6117-bayesian-optimization-with-robust-bayesian-neural-networks.pdf}
}
T. Simone Parisi, A. Blank, Tobias Viernickel, and T. Jan Peters: Local-utopia policy selection for multi-objective reinforcement learning. In Proceedings of the international symposium on adaptive dynamic programming and reinforcement learning (adprl), 2016. [Bibtex]

@InProceedings{ 16-simone-parisi-Local-utopia,
title = {{Local-utopia} policy selection for {multi-objective}
reinforcement learning},
author  = {\textbf{Simone Parisi} and Alexander Blank and Tobias
Viernickel and \textbf{Jan Peters}},
booktitle  = {Proceedings of the International Symposium on Adaptive
Dynamic Programming and Reinforcement Learning (ADPRL)},
year = {2016}
}
A. -S. Sheikh and J. Lücke: Select-and-sample for spike-and-slab sparse coding. In Advances in neural information processing systems (nips), 29, 3927–3935, 2016. [Bibtex]

@InProceedings{ 16-sheikh-Select-and-Sample,
title = {Select-and-Sample for Spike-and-Slab Sparse Coding},
author  = {A.-S. Sheikh and J. L{\"u}cke},
booktitle  = {Advances in Neural Information Processing Systems (NIPS)},
pages = {3927--3935},
volume  = {29},
year = {2016},
url = {http://papers.nips.cc/paper/6276-select-and-sample-for-spike-and-slab-sparse-coding}
}
T. Sebastian Gomez-Gonzalez, textbf{Gerhard Neumann}, B. Schölkopf, and T. Jan Peters: Using probabilistic movement primitives for striking movements. In International conference on humanoid robots (humanoids), 2016. [Bibtex]

@InProceedings{ 16-sebastian-gomez-gonzalez-Using,
title = {Using Probabilistic Movement Primitives for Striking
Movements},
author  = {\textbf{Sebastian Gomez-Gonzalez} and \textbf{Gerhard
Neumann} and Bernhard Sch{\"o}lkopf and \textbf{Jan Peters}},
booktitle  = {International Conference on Humanoid Robots (Humanoids)},
year = {2016},
url = {http://ieeexplore.ieee.org/document/7803322/}
}
T. Riad Akrour, A. Abdolmaleki, Hany Abdulsamad, and T. Gerhard Neumann: Model-free trajectory optimization for reinforcement learning. In International conference on machine learning (icml), 2016. [Bibtex]

@InProceedings{ 16-riad-akrour-Model-Free,
title = {Model-Free Trajectory Optimization for Reinforcement
Learning},
author  = {\textbf{Riad Akrour} and Abbas Abdolmaleki and Hany
Abdulsamad and \textbf{Gerhard Neumann}},
booktitle  = {International Conference on Machine Learning (ICML)},
year = {2016}
}
P. Perrone and N. Ay: Hierarchical quantification of synergy in channels. Frontiers in robotics and ai, 2, 35, 2016. [Bibtex]

@Article{ 16-perrone-Hierarchical-quantification-of-synergy-in-channels,
author  = {Paolo Perrone and Nihat Ay},
title = {{Hierarchical quantification of synergy in channels}},
doi = {10.3389/frobt.2015.00035},
journal  = {Frontiers in robotics and AI},
pages = {35},
year = {2016},
volume  = {2},
issn = {2296-9144}
}
B. Paassen, C. Göpfert, and B. Hammer: Gaussian process prediction for time series of structured data. In Proceedings of the esann, 24th european symposium on artificial neural networks, computational intelligence and machine learning, 2016. [Bibtex]

@InProceedings{ 16-paassen-Gaussian-process-prediction-for-time-series-of-structured-data,
abstract  = {Time series prediction constitutes a classic topic in
machine learning with wide-ranging applications, but mostly
restricted to the domain of vectorial sequence entries. In
recent years, time series of structured data (such as
sequences, trees or graph structures) have become more and
more important, for example in social network analysis or
intelligent tutoring systems. In this contribution, we
propose an extension of time series models to strucured
data based on Gaussian processes and structure kernels. We
also provide speedup techniques for predictions in linear
time, and we evaluate our approach on real data from the
domain of intelligent tutoring systems.},
author  = {Paassen, Benjamin and G{\"o}pfert, Christina and Hammer,
Barbara},
booktitle  = {Proceedings of the ESANN, 24th European Symposium on
Artificial Neural Networks, Computational Intelligence and
Machine Learning},
editor  = {Verleysen, Michele},
keyword  = {structured data, gaussian processes, time series
prediction},
location  = {Bruges},
title = {{Gaussian process prediction for time series of structured
data}},
year = {2016},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/paassen_goepfert_hammer_2016_gaussian_process_prediction_preprint.pdf}
}
B. Paassen, B. Mokbel, and B. Hammer: Adaptive structure metrics for automated feedback provision in intelligent tutoring systems. Neurocomputing, 192, Elsevier Science Publishers B. V., 2016. [Bibtex]

@Article{ 16-paassen-Adaptive-structure-metrics-for-automated-feedback-provision-in-intelligent-tutoring-systems,
abstract  = {Typical intelligent tutoring systems rely on detailed
domain-knowledge which is hard to obtain and difficult to
encode. As a data-driven alternative to explicit
domain-knowledge, one can present learners with feedback
based on similar existing solutions from a set of stored
examples. At the heart of such a data-driven approach is
the notion of similarity. We present a general-purpose
framework to construct structure metrics on sequential data
and to adapt those metrics using machine learning
techniques. We demonstrate that metric adaptation improves
the classification of wrong versus correct learner attempts
in a simulated data set from sports training, and the
classification of the underlying learner strategy in a real
Java programming dataset.},
author  = {Paassen, Benjamin and Mokbel, Bassam and Hammer, Barbara},
journal  = {Neurocomputing},
keyword  = {metric learning, intelligent tutoring systems, sequential
data, learning vector quantization, algebraic dynamic
programming},
publisher  = {Elsevier Science Publishers B. V.},
title = {{Adaptive structure metrics for automated feedback
provision in intelligent tutoring systems}},
doi = {doi:10.1016/j.neucom.2015.12.108},
volume  = {192},
year = {2016},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/paassen_mokbel_hammer_neurocomputing_2015_preprint.pdf}
}
Monk T., Savin C., and J. Lücke: Neurons equipped with intrinsic plasticity learn stimulus intensity statistics. In Advances in neural information processing systems, 29, 4278-4286, 2016. [Bibtex]

@InProceedings{ 16-monk-Neurons,
author  = {Monk, T., and Savin, C., and L{\"u}cke, J.},
title = {Neurons Equipped with Intrinsic Plasticity Learn Stimulus
Intensity Statistics},
booktitle  = {Advances in Neural Information Processing Systems},
pages = {4278-4286},
volume  = {29},
year = {2016},
url = {http://papers.nips.cc/paper/6582-neurons-equipped-with-intrinsic-plasticity-learn-stimulus-intensity-statistics}
}
R. Martín-Martín, S. Höfer, and O. Brock: An Integrated Approach to Visual Perception of Articulated Objects. In Proceedings of the 2016 IEEE International Conference on Robotics and Automotion (ICRA), in press, IEEE, 2016. [Bibtex]

@InProceedings{ 16-martín-martín-Integrated,
address  = {Stockholm, Sweden},
title = {An {Integrated} {Approach} to {Visual} {Perception} of
{Articulated} {Objects}},
booktitle  = {Proceedings of the 2016 {IEEE} {International}
{Conference} on {Robotics} and {Automotion} ({ICRA})},
publisher  = {IEEE},
author  = {Martín-Martín, Roberto and Höfer, Sebastian and Brock,
Oliver},
year = {2016},
pages = {in press},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/martin_hoefer_15_iros_sr_opt.pdf}
}
R. Martín-Martín, S. Höfer, and O. Brock: An integrated approach to visual perception of articulated objects. In Proceedings of the ieee international conference on robotics and automation, 5091-5097, 2016. [Bibtex]

@InProceedings{ 16-martin-martin-Integrated,
title = {An Integrated Approach to Visual Perception of Articulated
Objects},
author  = {Roberto {Mart{\'i}n-Mart{\'i}n} and Sebastian H\"ofer and
Oliver Brock},
booktitle  = {Proceedings of the IEEE International Conference on
Robotics and Automation},
pages = {5091 - 5097},
year = {2016},
location  = {Stockholm, Sweden},
month = {05},
pdf = {http://www.redaktion.tu-berlin.de/fileadmin/fg170/Publikationen\_pdf/martinmartin\_16\_icra.pdf},
url2 = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7487714},
projectname  = {Interactive Perception}
}
Q. Li, R. Haschke, and H. Ritter: Learning a tool’s homogeneous transformation by tactile-based interaction. In 2016 ieee-ras 16th international conference on humanoid robots (humanoids), 416-421, 2016. [Bibtex]

@InProceedings{ 16-li-Learning,
author  = {Qiang Li and Robert Haschke and Helge Ritter},
booktitle  = {2016 IEEE-RAS 16th International Conference on Humanoid
Robots (Humanoids)},
title = {Learning a tool's homogeneous transformation by
tactile-based interaction},
year = {2016},
pages = {416-421},
month = {Nov},
url = {https://ieeexplore.ieee.org/document/7803309}
}
J. Kulick: Information driven exploration in robotics. 2016. [Bibtex]

@PhDThesis{ 16-kulick-Information,
title = {Information driven exploration in robotics},
author  = {Kulick, Johannes},
year = {2016},
school  = {PhD Thesis, University of Stuttgart},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/papers/16-kulick-PhD.pdf}
}
T. Jost Tobias Springenberg: Unsupervised and semi-supervised learning with categorical generative adversarial networks. In International conference on learning representations (iclr), 2016. [Bibtex]

@InProceedings{ 16-jost-tobias-springenberg-Unsupervised,
author  = {\textbf{Jost Tobias Springenberg}},
title = {Unsupervised and Semi-supervised Learning with Categorical
Generative Adversarial Networks},
booktitle  = {International Conference on Learning Representations
(ICLR)},
year = {2016}
}
K. Ghazi-Zahedi, D. F. B. Haeufle, Guido Montúfar, S. Schmitt, and N. Ay: Evaluating morphological computation in muscle and DC-motor driven models of hopping movements. Frontiers in robotics and ai, 3, 42, 2016. [Bibtex]

@Article{ 16-ghazi-zahedi-Evaluating-morphological-computation-in-muscle-and-DC-motor-driven-models-of-hopping-movements,
author  = {Keyan Ghazi-Zahedi and Daniel F. B. Haeufle and Guido
Montúfar and Syn Schmitt and Nihat Ay},
title = {{Evaluating morphological computation in muscle and
DC-motor driven models of hopping movements}},
doi = {10.3389/frobt.2016.00042},
journal  = {Frontiers in robotics and AI},
pages = {42},
year = {2016},
volume  = {3},
issn = {2296-9144}
}
P. Englert and M. Toussaint: Combined optimization and reinforcement learning for manipulation skills. In Proc. of robotics: science and systems (r:ss 2016), 2016. [Accompanying Video] [Bibtex]

@InProceedings{ 16-englert-Combined,
author  = {Englert, Peter and Toussaint, Marc},
title = {Combined Optimization and Reinforcement Learning for
Manipulation Skills},
booktitle  = {Proc{.} of Robotics: Science and Systems (R:SS 2016)},
year = {2016},
youtube  = {bn_sv5A1BhQ},
pdf = {http://ipvs.informatik.uni-stuttgart.de/mlr/papers/16-englert-RSS.pdf}
}
C. Daniel, H. van Hoof, textbf{Jan Peters}, and T. Gerhard Neumann: Probabilistic inference for determining options in reinforcement learning. Machine learning, 2016. [Bibtex]

@Article{ 16-daniel-Probabilistic,
author  = "Christian Daniel and Herke van Hoof and \textbf{Jan
Peters} and \textbf{Gerhard Neumann}",
title = "Probabilistic Inference for Determining Options in
Reinforcement Learning",
journal  = "Machine Learning",
year = "2016",
url = "https://doi.org/10.1007/s10994-016-5580-x"
}
C. Daniel, T. Gerhard Neumann, O. Kroemer, and T. Jan Peters: Hierarchical relative entropy policy search. The journal of machine learning research, 2016. [Bibtex]

@Article{ 16-daniel-Hierarchical,
title = {Hierarchical relative entropy policy search},
author  = {Daniel, Christian and \textbf{Gerhard Neumann} and
Kroemer, Oliver and \textbf{Jan Peters}},
journal  = {The Journal of Machine Learning Research},
year = {2016}
}
W. Böhmer, R. Guo, and K. Obmerayer: Non-deterministic policy improvement stabilizes approximate reinforcement learning. 13th European Workshop on Reinforcement Learning, 2016. [Bibtex]

@Misc{ 16-bohmer-Non-deterministic,
author  = {B\"ohmer, Wendelin and Guo, Rong and Obmerayer, Klaus},
title = {Non-deterministic Policy Improvement Stabilizes
Approximate Reinforcement Learning},
year = {2016},
howpublished  = {13th European Workshop on Reinforcement Learning},
url = {https://ewrl.files.wordpress.com/2016/11/ewrl13-2016-submission_2.pdf}
}
R. Akrour, A. Abdolmaleki, H. Abdulsamad, and G. Neumann: Model-free trajectory optimization for reinforcement learning. In Proceedings of the 33rd international conference on machine learning, ICML 2016, new york, ny, usa, 19-24 june 2016, 2016. [Bibtex]

@InProceedings{ 16-akrour-Model-free,
title = {Model-free Trajectory Optimization for Reinforcement
Learning},
author  = {Riad Akrour and Abbas Abdolmaleki and Hany Abdulsamad and
Gerhard Neumann},
booktitle  = {Proceedings of the 33rd International Conference on
Machine Learning, {ICML} 2016, New York, NY, USA, 19-24
June 2016},
year = {2016},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/full_moto_16.pdf}
}

2015

M. Wistuba, N. Schilling, and L. Schmidt-Thieme: Hyperparameter search space pruning – a new component for sequential model-based hyperparameter optimization. In Proceedings of european conference on machine learning and principles and practice of knowledge discovery in databases (ecml’15), Springer, 2015. [Bibtex]

@InProceedings{ 15-wistuba-Hyperparameter,
title = {Hyperparameter Search Space Pruning - A New Component for
Sequential Model-Based Hyperparameter Optimization},
author  = {Wistuba, Martin and Schilling, Nicolas and Schmidt-Thieme,
Lars},
booktitle  = {Proceedings of European Conference on Machine Learning and
Principles and Practice of Knowledge Discovery in Databases
(ECML'15)},
year = {2015},
publisher  = {Springer},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/wistuba-ecml-2015.pdf}
}
T. textbf{Watter}, T. textbf{Springenberg}, T. textbf{Boedecker}, and T. textbf{Riedmiller}: Embed to control: a locally linear latent dynamics model for control from raw images. In Advances in neural information processing systems (nips), 2015. [Bibtex]

@InProceedings{ 15-watter-Embed,
title = {Embed to Control: A Locally Linear Latent Dynamics Model
for Control from Raw Images},
author  = {\textbf{Watter}, \textbf{Manuel} and
\textbf{Springenberg}, \textbf{Jost} and
\textbf{Boedecker}, \textbf{Joschka} and
\textbf{Riedmiller}, \textbf{Martin}},
booktitle  = {Advances in Neural Information Processing Systems (NIPS)},
year = {2015}
}
O. Walter, L. Drude, and R. Haeb-Umbach: Source counting in speech mixtures by nonparametric bayesian estimation of an infinite gaussian mixture model. In 40th international conference on acoustics, speech and signal processing (icassp 2015), 2015. [Bibtex]

@InProceedings{ 15-walter-Source,
title = {Source Counting in Speech Mixtures by Nonparametric
Bayesian Estimation of an infinite Gaussian Mixture Model},
author  = {Walter, Oliver and Drude, Lukas and Haeb-Umbach,
Reinhold},
booktitle  = {40th International Conference on Acoustics, Speech and
Signal Processing (ICASSP 2015)},
year = {2015},
month = {april},
abstract  = {In this paper we present a source counting algorithm to
determine the number of speakers in a speech mixture. In
our proposed method, we model the histogram of estimated
directions of arrival with a nonparametric Bayesian
infinite Gaussian mixture model. As an alternative to
classical model selection criteria and to avoid specifying
the maximum number of mixture components in advance, a
Dirichlet process prior is employed over the mixture
components. This allows to automatically determine the
optimal number of mixture components that most probably
model the observations. We demonstrate by experiments that
this model outperforms a parametric approach using a finite
Gaussian mixture model with a Dirichlet distribution prior
over the mixture weights.},
comment  = {[Poster]},
url = {http://nt.uni-paderborn.de/public/pubs/2015/WaDrHa15.pdf},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/WaDrHa15.pdf}
}
O. Walter, R. Haeb-Umbach, J. Strunk, and N. P. Himmelmann: Lexicon discovery for language preservation using unsupervised word segmentation with pitman-yor language models. 2015. [Bibtex]

@TechReport{ 15-walter-Lexicon,
title = {Lexicon Discovery for Language Preservation using
Unsupervised Word Segmentation with Pitman-Yor Language
Models},
author  = {Walter, Oliver and Haeb-Umbach, Reinhold and Strunk, Jan
and P. Himmelmann, Nikolaus },
institution  = {University of Paderborn Department of Communications
Engineering},
year = {2015},
number  = {FGNT-2015-01},
type = {FGNT Technical Report},
abstract  = {In this paper we show that recently developed algorithms
for unsupervised word segmentation can be a valuable tool
for the documentation of endangered languages. We applied
an unsupervised word segmentation algorithm based on a
nested Pitman-Yor language model to two austronesian
languages, Wooi and Waima'a. The algorithm was then
modified and parameterized to cater the needs of linguists
for high precision of lexical discovery: We obtained a
lexicon precision of of 69.2\% and 67.5\% for Wooi and
Waima'a, respectively, if single-letter words and words
found less than three times were discarded. A comparison
with an English word segmentation task showed comparable
performance, verifying that the assumptions underlying the
Pitman-Yor language model, the universality of Zipf's law
and the power of n-gram structures, do also hold for
languages as exotic as Wooi and Waima'a.},
url = {http://nt.uni-paderborn.de/public/pubs/2015/WaHaStHi.pdf},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/WaHaStHi.pdf}
}
O. Walter, R. Häb-Umbach, B. Mokbel, B. Paaßen, and B. Hammer: Autonomous learning of representations. Ki – künstliche intelligenz, 1–13, Springer Berlin Heidelberg, 2015. [Bibtex]

@Article{ 15-walter-Autonomous,
abstract  = {Besides the core learning algorithm itself, one major
question in machine learning is how to best encode given
training data such that the learning technology can
efficiently learn based thereon and generalize to novel
data. While classical approaches often rely on a hand coded
data representation, the topic of autonomous representation
or feature learning plays a major role in modern learning
architectures. The goal of this contribution is to give an
overview about different principles of autonomous feature
learning, and to exemplify two principles based on two
recent examples: autonomous metric learning for sequences,
and autonomous learning of a deep representation for spoken
language, respectively.},
author  = {Walter, Oliver and H{\"a}b-Umbach, Reinhold and Mokbel,
Bassam and Paa{\ss}en, Benjamin and Hammer, Barbara},
issn = {0933-1875},
journal  = {KI - K{\"u}nstliche Intelligenz},
language  = {English},
pages = {1--13},
publisher  = {Springer Berlin Heidelberg},
title = {Autonomous Learning of Representations},
url = {http://link.springer.com/article/10.1007/s13218-015-0372-1},
doi = {10.1007/s13218-015-0372-1},
year = {2015},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/Learning_Representations.pdf}
}
M. Toussaint, H. Ritter, and O. Brock: The optimization route to robotics and alternatives. Ki – künstliche intelligenz, 29, 379-388, Springer Berlin Heidelberg, 2015. [Bibtex]

@Article{ 15-toussaint-Optimization,
year = {2015},
issn = {0933-1875},
journal  = {KI - Künstliche Intelligenz},
volume  = {29},
number  = {4},
doi = {10.1007/s13218-015-0379-7},
title = {The Optimization Route to Robotics and Alternatives},
url = {http://link.springer.com/article/10.1007/s13218-015-0379-7},
publisher  = {Springer Berlin Heidelberg},
author  = {Toussaint, Marc and Ritter, Helge and Brock, Oliver},
pages = {379-388},
language  = {English},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/15-toussaint-KI.pdf}
}
T. Simone Parisi, H. Abdulsamad, Alexandros Paraschos, C. Daniel, and T. Jan Peters: Reinforcement learning vs human programming in tetherball robot games. In Proceedings of the international conference on intelligent robots and systems (iros), 2015. [Bibtex]

@InProceedings{ 15-simone-parisi-Reinforcement,
title = {Reinforcement Learning vs Human Programming in Tetherball
Robot Games},
author  = {\textbf{Simone Parisi} and Hany Abdulsamad and Alexandros
Paraschos and Christian Daniel and \textbf{Jan Peters}},
booktitle  = {Proceedings of the International Conference on Intelligent
Robots and Systems (IROS)},
year = {2015}
}
N. Schilling, M. Wistuba, L. Drumond, and L. Schmidt-Thieme: Hyperparameter optimization with factorized multilayer perceptrons. In Proceedings of european conference on machine learning and principles and practice of knowledge discovery in databases (ecml’15), Springer, 2015. [Bibtex]

@InProceedings{ 15-schilling-Hyperparameter,
title = {Hyperparameter Optimization with Factorized Multilayer
Perceptrons},
author  = {Schilling, Nicolas and Wistuba, Martin and Drumond, Lucas
and Schmidt-Thieme, Lars},
booktitle  = {Proceedings of European Conference on Machine Learning and
Principles and Practice of Knowledge Discovery in Databases
(ECML'15)},
year = {2015},
publisher  = {Springer},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/schilling-ecml-2015.pdf}
}
S. Parisi, H. Abdulsamad, A. Paraschos, C. Daniel, and J. Peters: Reinforcement learning vs human programming in tetherball robot games. Proceedings of the ieee/rsj conference on intelligent robots and systems (iros), 2015. [Bibtex]

@InProceedings{ 15-parisi-Reinforcement,
author  = "Parisi, S. and Abdulsamad, H. and Paraschos, A. and
Daniel, C. and Peters, J.",
year = "2015",
title = "Reinforcement Learning vs Human Programming in Tetherball
Robot Games",
journal  = "Proceedings of the IEEE/RSJ Conference on Intelligent
Robots and Systems (IROS)",
key = "scarl",
url = "http://www.ausy.tu-darmstadt.de/uploads/Team/SimoneParisi/parisi_iros_2015",
crossref  = "p10833",
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/parisi_iros_2015.pdf}
}
B. Paaßen: Interview with werner von seelen. Ki – künstliche intelligenz, 29, 445-448, Springer Berlin Heidelberg, 2015. [Bibtex]

@Article{ 15-paaßen-Interview,
year = {2015},
issn = {0933-1875},
journal  = {KI - Künstliche Intelligenz},
volume  = {29},
number  = {4},
doi = {10.1007/s13218-015-0368-x},
title = {Interview with Werner von Seelen},
url = {http://dx.doi.org/10.1007/s13218-015-0368-x},
publisher  = {Springer Berlin Heidelberg},
author  = {Paaßen, Benjamin},
pages = {445-448},
language  = {English},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/interview_werner_von_seelen.pdf}
}
B. Paaßen, B. Mokbel, and B. Hammer: Adaptive structure metrics for automated feedback provision in java programming. 307–312, 2015. [Bibtex]

@InProceedings{ 15-paaen-Adaptive,
abstract  = {Today's learning supporting systems for programming mostly
rely on pre-coded feedback provision, such that their
applicability is restricted to modelled tasks. In this
contribution, we investigate the suitability of machine
learning techniques to automate this process by means of a
presentationm of similar solution strategies from a set of
stored examples. To this end we apply structure metric
learning methods in local and global alignment which can be
used to compare Java programs. We demonstrate that
automatically adapted metrics better identify the
underlying programming strategy as compared to their
default counterparts in a benchmark example from
programming.},
author  = {Paa{\ss}en, Benjamin and Mokbel, Bassam and Hammer,
Barbara},
editor  = {Michel Verleysen},
language  = {English},
location  = {Bruges, Belgium},
pages = {307--312},
title = {Adaptive structure metrics for automated feedback
provision in Java programming},
year = {2015},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/paassen_mokbel_hammer_adaptive_structure_metrics.pdf}
}
P. Ochs, R. Ranftl, T. Brox, and T. Pock: Bilevel optimization with nonsmooth lower level problems. In International conference on scale space and variational methods in computer vision (ssvm), 2015. [Bibtex]

@InProceedings{ 15-ochs-Bilevel,
author  = {P. Ochs and R. Ranftl and T. Brox and T. Pock},
title = {Bilevel Optimization with Nonsmooth Lower Level Problems},
booktitle  = {International Conference on Scale Space and Variational
Methods in Computer Vision (SSVM)},
year = {2015},
url = {http://lmb.informatik.uni-freiburg.de/Publications/2015/OB15a},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/2015_ochs_ssvm-bilevel.pdf}
}
G. Montúfar, N. Ay, and K. Ghazi-Zahedi: Geometry and expressive power of conditional restricted Boltzmann machines. Journal of machine learning research, 16, 2405–2436, 2015. [Bibtex]

@Article{ 15-montúfar-Geometry-and-expressive-power-of-conditional-restricted-Boltzmann-machines,
author  = {Guido Montúfar and Nihat Ay and Keyan Ghazi-Zahedi},
title = {{Geometry and expressive power of conditional restricted
Boltzmann machines}},
journal  = {Journal of machine learning research},
pages = {2405--2436},
year = {2015},
volume  = {16},
issn = {1533-7928}
}
G. Montúfar, K. Ghazi-Zahedi, and N. Ay: A theory of cheap control in embodied systems. Plos computational biology, 11, e1004427, 2015. [Bibtex]

@Article{ 15-montúfar-A-theory-of-cheap-control-in-embodied-systems,
author  = {Guido Montúfar and Keyan Ghazi-Zahedi and Nihat Ay},
title = {{A theory of cheap control in embodied systems}},
doi = {10.1371/journal.pcbi.1004427},
journal  = {PLoS computational biology},
pages = {e1004427},
year = {2015},
volume  = {11},
number  = {9},
issn = {1553-7358}
}
B. Mokbel, B. Paassen, Frank-Michael Schleif, and B. Hammer: Metric learning for sequences in relational LVQ. Neurocomputing, (accepted/in press), Elsevier Science Publishers B. V., 2015. [Bibtex]

@Article{ 15-mokbel-Metric,
author  = {Bassam Mokbel and Benjamin Paassen and Frank-Michael
Schleif and Barbara Hammer},
title = {Metric learning for sequences in relational {LVQ}},
journal  = {Neurocomputing},
publisher  = {Elsevier Science Publishers B. V.},
address  = {Amsterdam, The Netherlands},
year = {2015},
volume  = {(accepted/in press)},
pages = {},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/MokbelEtAl2015-Neurocomputing-MetricLearningSequencesLVQ.pdf}
}
M. Missura and S. Behnke: Online learning of bipedal walking stabilization. Ki – künstliche intelligenz, 29, 401–405, Springer Berlin Heidelberg, 2015. [Bibtex]

@Article{ 15-missura-Online,
author  = {Marcell Missura and Sven Behnke},
title = {Online Learning of Bipedal Walking Stabilization},
publisher  = {Springer Berlin Heidelberg},
journal  = {KI - Künstliche Intelligenz},
volume  = {29},
number  = {4},
pages = {401--405},
year = {2015},
issn = {0933-1875},
url = {http://link.springer.com/article/10.1007/s13218-015-0387-7},
doi = {10.1007/s13218-015-0387-7},
language  = {English},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/2015_ki_Missura.pdf}
}
Q. Li, R. Haschke, and H. Ritter: Visuo-tactile servoing for exploration and manipulation. In Iros2015 ws:see and touch: 1st workshop on multimodal sensor-based robot control for hri and soft manipulation, IEEE, 2015. [Bibtex]

@Conference{ 15-li-Visuo-Tactile,
title = {Visuo-Tactile Servoing for Exploration and Manipulation},
booktitle  = {IROS2015 WS:See and Touch: 1st Workshop on multimodal
sensor-based robot control for HRI and soft manipulation},
year = {2015},
month = {28/09/2015},
publisher  = {IEEE},
organization  = {IEEE},
address  = {Hamburg, Germany},
author  = {Qiang Li and Robert Haschke and Helge Ritter},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/iros15ws.pdf}
}
Q. Li, R. Haschke, and H. Ritter: Towards body schema learning using training data acquired by continuous self-touch. In Ieee ras humanoids conference, IEEE, 2015. [Bibtex]

@Conference{ 15-li-Towards,
title = {Towards Body Schema Learning using Training Data Acquired
by Continuous Self-touch},
booktitle  = {IEEE RAS Humanoids Conference},
year = {2015},
month = {3/11/2015},
publisher  = {IEEE},
organization  = {IEEE},
address  = {Seoul, Korea},
author  = {Qiang Li and Robert Haschke and Helge Ritter},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/calib.pdf}
}
J. Kulick, R. Lieck, and M. Toussaint: The advantage of cross-entropy over entropy in iterative information gathering. 2015. [Bibtex]

@Misc{ 15-kulick-Advantage,
key = "MaxCE",
author  = "Johannes Kulick and Robert Lieck and Marc Toussaint",
year = "2015",
title = {The Advantage of Cross-Entropy over Entropy in Iterative
Information Gathering},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/1409.7552v2.pdf}
}
J. Kulick, S. Otte, and M. Toussaint: Robots solving serial means-means-end problems. RSS Workshop on Combining AI Reasoning and Cognitive Science, 2015. [Bibtex]

@Misc{ 15-kulick-Robots,
key = "RSSws",
author  = "J. Kulick and S. Otte and M. Toussaint",
title = "Robots Solving Serial Means-Means-End Problems",
howpublished  = "RSS Workshop on Combining AI Reasoning and Cognitive
Science",
year = "2015",
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/15-kulick-RSSws.pdf}
}
J. Kulick, S. Otte, and M. Toussaint: Active exploration of joint dependency structures. In Proc. of the ieee int. conf. on robotics and automation (icra 2015), 2015. [Bibtex]

@InProceedings{ 15-kulick-Active,
key = "ICRA",
author  = "Johannes Kulick and Stefan Otte and Marc Toussaint",
title = "Active Exploration of Joint Dependency Structures",
booktitle  = icra # " (ICRA 2015)",
year = "2015",
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/15-kulick-ICRA.pdf}
}
K. Kersting and S. Natarajan: Statistical relational artificial intelligence: from distributions through actions to optimization. Ki – künstliche intelligenz, 29, 363-368, Springer Berlin Heidelberg, 2015. [Bibtex]

@Article{ 15-kersting-Statistical,
year = {2015},
issn = {0933-1875},
journal  = {KI - Künstliche Intelligenz},
volume  = {29},
number  = {4},
title = {Statistical Relational Artificial Intelligence: From
Distributions through Actions to Optimization},
publisher  = {Springer Berlin Heidelberg},
author  = {Kersting,Kristian and Natarajan, Sriraam},
pages = {363-368},
url = {http://link.springer.com/article/10.1007/s13218-015-0386-8},
doi = {10.1007/s13218-015-0386-8},
language  = {English},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/kersting2015ki-1.pdf}
}
F. Hutter, J. Lücke, and L. Schmidt-Thieme: Beyond manual tuning of hyperparameters. Ki – künstliche intelligenz, in press, Springer, 2015. [Bibtex]

@Article{ 15-hutter-Beyond,
title = {Beyond Manual Tuning of Hyperparameters},
author  = {Hutter, Frank and L{\"u}cke, J{\"o}rg and Schmidt-Thieme,
Lars},
journal  = {KI -- K{\"u}nstliche Intelligenz},
pages = {in press},
year = {2015},
publisher  = {Springer},
url = {http://link.springer.com/article/10.1007/s13218-015-0381-0}
}
S. Gross and N. Pinkwart: Towards an integrative learning environment for java programming. In Ieee 15th international conference on advanced learning technologies (icalt), 2015, 24-28, IEEE Computer Society Press, 2015. [Bibtex]

@InProceedings{ 15-gross-Towards,
author  = {Gross, S. and Pinkwart, N.},
booktitle  = {IEEE 15th International Conference on Advanced Learning
Technologies (ICALT), 2015},
title = {Towards an Integrative Learning Environment for Java
Programming},
year = {2015},
pages = {24-28},
keywords  = {Education;Java;Learning
systems;Problem-solving;Programming
environments;Programming profession;Java;adaptive
tutoring;integrative;learning environment;programming},
doi = {10.1109/ICALT.2015.75},
month = {July},
publisher  = {IEEE Computer Society Press},
editor  = {Sampson, D. G. and Huang, R. and Hwang, G.-J. and Liu,
T.-Z. and Chen, N.-S. and Kinshuk and Tsai, C.-C.},
address  = {Los Alamitos, CA},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7265253},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/ICALT_Paper_2015.pdf}
}
S. Gross, B. Mokbel, B. Hammer, and N. Pinkwart: Learning feedback in intelligent tutoring systems. Ki – künstliche intelligenz, 1–6, Springer Berlin Heidelberg, 2015. [Bibtex]

@Article{ 15-gross-Learning,
abstract  = {Intelligent Tutoring Systems (ITSs) are adaptive learning
systems that aim to support learners by providing
one-on-one individualized instruction. Typically,
instructing learners in ITSs is build on formalized domain
knowledge and, thus, the applicability is restricted to
well-defined domains where knowledge about the domain being
taught can be explicitly modeled. For ill-defined domains,
human tutors still by far outperform the performance of
ITSs, or the latter are not applicable at all. As part of
the DFG priority programme {\textacutedbl}Autonomous
Learning{\textacutedbl}, the FIT project has been conducted
over a period of 3 years pursuing the goal to develop novel
ITS methods, that are also applicable for ill-defined
problems, based on implicit domain knowledge extracted from
educational data sets. Here, machine learning techniques
have been used to autonomously infer structures from given
learning data (e.g., student solutions) and, based on these
structures, to develop strategies for instructing learners.},
author  = {Gross, Sebastian and Mokbel, Bassam and Hammer, Barbara
and Pinkwart, Niels},
issn = {0933-1875},
journal  = {KI - K{\"u}nstliche Intelligenz},
language  = {English},
pages = {1--6},
publisher  = {Springer Berlin Heidelberg},
title = {Learning Feedback in Intelligent Tutoring Systems},
url = {http://link.springer.com/article/10.1007/s13218-015-0367-y},
doi = {10.1007/s13218-015-0367-y},
year = {2015},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/KI_Journal_Article_2015_revised.pdf}
}
S. Gross and N. Pinkwart: How do learners behave in help-seeking when given a choice?. In Artificial intelligence in education, 9112, 600-603, Springer International Publishing, 2015. [Bibtex]

@InProceedings{ 15-gross-How,
title = {How Do Learners Behave in Help-Seeking When Given a
Choice?},
author  = {Gross, S. and Pinkwart, N.},
booktitle  = {Artificial Intelligence in Education},
publisher  = {Springer International Publishing},
year = {2015},
editor  = {Conati, Cristina and Heffernan, Neil and Mitrovic,
Antonija and Verdejo, M. Felisa},
pages = {600-603},
series  = {Lecture Notes in Computer Science},
volume  = {9112},
doi = {10.1007/978-3-319-19773-9_71},
isbn = {978-3-319-19772-2},
keywords  = {Intelligent tutoring system; Help-seeking; Feedback
choice},
url = {http://link.springer.com/chapter/10.1007%2F978-3-319-19773-9_71},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/AIED_Poster_2015.pdf}
}
M. Feurer, T. Springenberg, and F. Hutter: Initializing bayesian hyperparameter optimization via meta-learning. In Proceedings of the twenty-ninth aaai conference on artificial intelligence, 2015. [Bibtex]

@InProceedings{ 15-feurer-Initializing,
lauthor  = {Matthias Feurer and Tobias Springenberg and Frank Hutter},
author  = {M. Feurer and T. Springenberg and F. Hutter},
title = {Initializing Bayesian Hyperparameter Optimization via
Meta-Learning},
booktitle  = {Proceedings of the Twenty-Ninth AAAI Conference on
Artificial Intelligence},
conference  = {AAAI Conference},
year = {2015},
month = jan
}
M. Feurer, A. Klein, K. Eggensperger, J.~T. Springenberg, M. Blum, and F. Hutter: Efficient and robust automated machine learning. In Proceedings of the 29th international conference on advances in neural information processing systems, 2962-2970, 2015. [Bibtex]

@InProceedings{ 15-feurer-Efficient,
title = {Efficient and Robust Automated Machine Learning},
author  = {M. Feurer and A. Klein and K. Eggensperger and J.~T.
Springenberg and M. Blum and F. Hutter},
year = {2015},
editor  = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and
R. Garnett},
booktitle  = {Proceedings of the 29th International Conference on
Advances in Neural Information Processing Systems},
pages = {2962-2970},
url = {https://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf}
}
P. Englert and M. Toussaint: Inverse KKT – Learning Cost Functions of Manipulation Tasks from Demonstrations. In Proceedings of the international symposium of robotics research, 2015. [Bibtex]

@InProceedings{ 15-englert-Inverse-KKT--Learning-Cost-Functions-of-Manipulation-Tasks-from-Demonstrations,
author  = {Englert, Peter and Toussaint, Marc},
booktitle  = {Proceedings of the International Symposium of Robotics
Research},
date-added  = {2017-10-24 14:28:20 +0000},
date-modified  = {2017-10-24 14:28:20 +0000},
title = {{Inverse KKT -- Learning Cost Functions of Manipulation
Tasks from Demonstrations}},
year = {2015},
pdf = {https://journals.sagepub.com/doi/pdf/10.1177/0278364917745980}
}
A. Eitel, J. T. Springenberg, Luciano Spinello, M. Riedmiller, and W. Burgard: Multimodal deep learning for robust rgb-d object recognition. In Ieee/rsj international conference on intelligent robots and systems (iros), 2015. [Bibtex]

@InProceedings{ 15-eitel-Multimodal,
author  = {Andreas Eitel and Jost Tobias Springenberg and Luciano
Spinello and Martin Riedmiller and Wolfram Burgard},
title = {Multimodal Deep Learning for Robust RGB-D Object
Recognition},
booktitle  = {IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS)},
year = 2015,
address  = {Hamburg, Germany},
url = {http://ais.informatik.uni-freiburg.de/publications/papers/eitel15iros.pdf},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/eitel15iros.pdf}
}
T. Domhan, J. Springenberg, and F. Hutter: Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves. In Proceedings of the 25th international joint conference on artificial intelligence (ijcai’15), 3460–3468, 2015. [Bibtex]

@InProceedings{ 15-domhan-Speeding,
author  = {T. Domhan and J.. Springenberg and F. Hutter},
title = {Speeding Up Automatic Hyperparameter Optimization of Deep
Neural Networks by Extrapolation of Learning Curves},
editor  = {Q. Yang and M. Wooldridge},
booktitle  = {Proceedings of the 25th International Joint Conference on
Artificial Intelligence (IJCAI'15)},
year = {2015},
pages = {3460--3468},
url = {https://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/view/11468/11222}
}
V. Despotovic, O. Walter, and R. Haeb-Umbach: Semantic analysis of spoken input using markov logic networks. In Interspeech 2015, 2015. [Bibtex]

@InProceedings{ 15-despotovic-Semantic,
title = {Semantic Analysis of Spoken Input using Markov Logic
Networks},
author  = {Despotovic, Vladimir and Walter, Oliver and Haeb-Umbach,
Reinhold},
booktitle  = {INTERSPEECH 2015},
year = {2015},
month = {oct},
abstract  = {We present a semantic analysis technique for spoken input
using Markov Logic Networks (MLNs). MLNs combine graphical
models with first-order logic. They areparticularly
suitable for providing inference in the presence of
inconsistent and incomplete data, which are typical of an
automatic speech recognizer's (ASR) output in the presence
of degraded speech. The target application is a speech
interface to a home automation system to be operated by
people with speech impairments, where the ASR output is
particularly noisy. In order to cater for dysarthric speech
with non-canonical phoneme realizations, acoustic
representations of the input speech are learned in an
unsupervised fashion. While training data transcripts are
not required for the acoustic model training, the MLN
training requires supervision, however, at a rather loose
and abstract level. Results on two databases, one of them
for dysarthric speech, show that MLN-based semantic
analysis clearly outperforms baseline approaches employing
non-negative matrix factorization, multinomial naive Bayes
models, or support vector machines.},
comment  = {[Poster]},
url = {http://nt.uni-paderborn.de/public/pubs/2015/DeWaHa.pdf},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/DeWaHa.pdf}
}
W. Böhmer, J. Springenberg, J. Boedecker, M. Riedmiller, and K. Obermayer: Autonomous learning of state representations for control: an emerging field aims to autonomously learn state representations for reinforcement learning agents from their real-world sensor observations. Ki – künstliche intelligenz, 352–362, Springer Berlin Heidelberg, 2015. [Bibtex]

@Article{ 15-böhmer-Autonomous,
author  = {Böhmer, Wendelin and Springenberg, JostTobias and
Boedecker, Joschka and Riedmiller, Martin and Obermayer,
Klaus},
title = {Autonomous Learning of State Representations for Control:
An Emerging Field Aims to Autonomously Learn State
Representations for Reinforcement Learning Agents from
Their Real-World Sensor Observations},
doi = {10.1007/s13218-015-0356-1},
journal  = {KI - Künstliche Intelligenz},
publisher  = {Springer Berlin Heidelberg},
pages = {352--362},
issn = {0933-1875},
year = {2015},
keywords  = {End-to-end reinforcement learning; Representation
learning; Deep auto-encoder networks; Slow feature
analysis; Autonomous robotics},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/Boehmer15a-ki.pdf}
}
W. Böhmer and K. Obermayer: Regression with linear factored functions. In Machine learning and knowledge discovery in databases, 9284, 119-134, Springer International Publishing, 2015. [Bibtex]

@InCollection{ 15-bohmer-Regression,
year = {2015},
booktitle  = {Machine Learning and Knowledge Discovery in Databases},
volume  = {9284},
series  = {Lecture Notes in Computer Science},
title = {Regression with Linear Factored Functions},
publisher  = {Springer International Publishing},
author  = {B\"ohmer, Wendelin and Obermayer, Klaus},
pages = {119-134},
url = {http://www.ni.tu-berlin.de/fileadmin/fg215/articles/boehmer2015b.pdf}
}
W. Böhmer, T. T. textbf{Springenberg}, T. textbf{Boedecker}, T. textbf{Riedmiller}, and K. Obermayer: Autonomous learning of state representations for control: an emerging field aims to autonomously learn state representations for reinforcement learning agents from their real-world sensor observations. Ki – künstliche intelligenz, 29, 2015. [Bibtex]

@Article{ 15-bohmer-Autonomous,
author  = {B{\"o}hmer, Wendelin and \textbf{Springenberg},
\textbf{Jost Tobias} and \textbf{Boedecker},
\textbf{Joschka} and \textbf{Riedmiller}, \textbf{Martin}
and Obermayer, Klaus},
title = "Autonomous Learning of State Representations for Control:
An Emerging Field Aims to Autonomously Learn State
Representations for Reinforcement Learning Agents from
Their Real-World Sensor Observations",
journal  = "KI - K{\"u}nstliche Intelligenz",
year = "2015",
month = "Nov",
day = "01",
volume  = "29",
number  = "4"
}
W. Boehmer and K. Obermayer: Regression with linear factored functions. machine learning and knowledge discovery in databases. Lecture notes in computer science, 9284, 119-134, 2015. [Bibtex]

@Article{ 15-boehmer-Regression,
author  = {Boehmer, Wendelin and Obermayer, Klaus},
title = {Regression with Linear Factored Functions. Machine
Learning and Knowledge Discovery in Databases},
journal  = {Lecture Notes in Computer Science},
volume  = {9284},
pages = {119-134},
year = {2015},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/Boehmer15b-ecml.pdf}
}
N. Ay and W. Löhr: The Umwelt of an embodied agent\,:\,a measure-theoretic definition. Theory in biosciences, 134, 105–116, 2015. [Bibtex]

@Article{ 15-ay-The-Umwelt-of-an-embodied-agent-a-measure-theoretic-definition,
author  = {Nihat Ay and Wolfgang Löhr},
title = {{The Umwelt of an embodied agent\,:\,a measure-theoretic
definition}},
doi = {10.1007/s12064-015-0217-3},
journal  = {Theory in biosciences},
pages = {105--116},
year = {2015},
volume  = {134},
number  = {3-4},
issn = {1431-7613}
}
N. Ay: Information geometry on complexity and stochastic interaction. Entropy, 17, 2432–2458, 2015. [Bibtex]

@Article{ 15-ay-Information-geometry-on-complexity-and-stochastic-interaction,
author  = {Nihat Ay},
title = {{Information geometry on complexity and stochastic
interaction}},
doi = {10.3390/e17042432},
journal  = {Entropy},
pages = {2432--2458},
year = {2015},
volume  = {17},
number  = {4},
issn = {1099-4300}
}
N. Ay: Geometric design principles for brains of embodied agents. Künstliche intelligenz\,:\,ki, 29, 389–399, 2015. [Bibtex]

@Article{ 15-ay-Geometric-design-principles-for-brains-of-embodied-agents,
author  = {Nihat Ay},
title = {{Geometric design principles for brains of embodied
agents}},
doi = {10.1007/s13218-015-0382-z},
journal  = {Künstliche Intelligenz\,:\,KI},
pages = {389--399},
year = {2015},
volume  = {29},
number  = {4},
issn = {0933-1875}
}
N. Ay: Geometric design principles for brains of embodied agents. Ki – künstliche intelligenz, 29, 389-399, Springer Berlin Heidelberg, 2015. [Bibtex]

@Article{ 15-ay-Geometric,
year = {2015},
issn = {0933-1875},
journal  = {KI - Künstliche Intelligenz},
volume  = {29},
number  = {4},
title = {Geometric Design Principles for Brains of Embodied
Agents},
publisher  = {Springer Berlin Heidelberg},
author  = {Ay, Nihat},
pages = {389-399},
url = {http://link.springer.com/article/10.1007/s13218-015-0382-z},
doi = {10.1007/s13218-015-0382-z},
language  = {English},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/Ay.pdf}
}

2014

O. Walter, V. Despotovic, R. Haeb-Umbach, J. Gemmeke, B. Ons, and H. Van hamme: An evaluation of unsupervised acoustic model training for a dysarthric speech interface. In Interspeech 2014, 2014. [Bibtex]

@InProceedings{ 14-walter-Evaluation,
title = {An Evaluation of Unsupervised Acoustic Model Training for
a Dysarthric Speech Interface},
author  = {Walter, Oliver and Despotovic, Vladimir and Haeb-Umbach,
Reinhold and Gemmeke, Jrt and Ons, Bart and Van hamme,
Hugo},
booktitle  = {INTERSPEECH 2014},
year = {2014},
abstract  = {In this paper, we investigate unsupervised acoustic model
training approaches for dysarthric-speech recognition.
These models are first, frame-based Gaussian
posteriorgrams, obtained from Vector Quantization (VQ),
second, so-called Acoustic Unit Descriptors (AUDs), which
are hidden Markov models of phone-like units, that are
trained in an unsupervised fashion, and, third,
posteriorgrams computed on the AUDs. Experiments were
carried out on a database collected from a home automation
task and containing nine speakers, of which seven are
considered to utter dysarthric speech. All unsupervised
modeling approaches delivered significantly better
recognition rates than a speaker-independent phoneme
recognition baseline, showing the suitability of
unsupervised acoustic model training for dysarthric speech.
While the AUD models led to the most compact representation
of an utterance for the subsequent semantic inference
stage, posteriorgram-based representations resulted in
higher recognition rates, with the Gaussian posteriorgram
achieving the highest slot filling F-score of 97.02%. Index
Terms: unsupervised learning, acoustic unit descriptors,
dysarthric speech, non-negative matrix factorization},
comment  = {[Poster]
[Spotlight]},
url = {http://nt.uni-paderborn.de/public/pubs/2014/WaDeHaebGeOnVa14.pdf},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/WaDeHaebGeOnVa14.pdf}
}
N. A. Vien and M. Toussaint: Model-based relational rl when object existence is partially observable. In Proc. of the int. conf. on machine learning (icml 2014), 2014. [Bibtex]

@InProceedings{ 14-vien-Model-Based,
key = "ICML",
author  = "Ngo Anh Vien and Marc Toussaint",
title = "Model-Based Relational RL When Object Existence is
Partially Observable",
booktitle  = icml # " (ICML 2014)",
year = "2014",
url = {https://ipvs.informatik.uni-stuttgart.de/mlr/marc/publications/14-vien-ICML.pdf},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/14-vien-ICML.pdf}
}
S. Otte, J. Kulick, M. Toussaint, and O. Brock: Entropy based strategies for physical exploration of the environment’s degrees of freedom. In Proc. of the int. conf. on intelligent robots and systems (iros 2014), 2014. [Bibtex]

@InProceedings{ 14-otte-Entropy,
key = "IROS",
author  = "S. Otte and J. Kulick and M. Toussaint and O. Brock",
title = "Entropy Based Strategies for Physical Exploration of the
Environment's Degrees of Freedom",
booktitle  = iros # " (IROS 2014)",
year = "2014",
url = {https://ipvs.informatik.uni-stuttgart.de/mlr/marc/publications/14-otte-IROS.pdf},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/14-otte-IROS.pdf}
}
B. Mokbel, B. Paassen, and B. Hammer: Efficient adaptation of structure metrics in prototype-based classification. In Artificial neural networks and machine learning – ICANN 2014 – 24th international conference on artificial neural networks, hamburg, germany, september 15-19, 2014. proceedings, 8681, 571–578, Springer, 2014. [Bibtex]

@InProceedings{ 14-mokbel-Efficient,
author  = {Mokbel, Bassam and Paassen, Benjamin and Hammer, Barbara},
title = {Efficient Adaptation of Structure Metrics in
Prototype-Based Classification},
booktitle  = {Artificial Neural Networks and Machine Learning - {ICANN}
2014 - 24th International Conference on Artificial Neural
Networks, Hamburg, Germany, September 15-19, 2014.
Proceedings},
year = {2014},
pages = {571--578},
url = {http://dx.doi.org/10.1007/978-3-319-11179-7_72},
doi = {10.1007/978-3-319-11179-7_72},
series  = {Lecture Notes in Computer Science},
volume  = {8681},
publisher  = {Springer},
isbn = {978-3-319-11178-0},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/MokbelEtAl2014-ICANN2014-EfficientAdaptiveAlignmentGLVQ-1.pdf}
}
B. Mokbel, B. Paassen, and B. Hammer: Adaptive distance measures for sequential data. In 22th european symposium on artificial neural networks, computational intelligence and machine learning, ESANN 2014, bruges, belgium, april 23-25, 2014, 265-270, i6doc.com, 2014. [Bibtex]

@InProceedings{ 14-mokbel-Adaptive,
author  = {Mokbel, Bassam and Paassen, Benjamin and Hammer, Barbara},
title = {Adaptive distance measures for sequential data},
booktitle  = {22th European Symposium on Artificial Neural Networks,
Computational Intelligence and Machine Learning, {ESANN}
2014, Bruges, Belgium, April 23-25, 2014},
pages = {265-270},
year = {2014},
editor  = {Michel Verleysen},
publisher  = {i6doc.com},
url = {http://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2014-82.pdf},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/MokbelEtAl2014-ESANN2014-AdaptiveAlignmentGLVQ-1}
}
M. Mladenov, A. Globerson, and K. Kersting: Lifted message passing as reparametrization of graphical models. In In proceedings of the 30th conference on uncertainty in artificial intelligence (uai), 2014. [Bibtex]

@InProceedings{ 14-mladenov-Lifted,
author  = { Mladenov, Martin and Globerson, Amir and Kersting,
Kristian},
title = {Lifted Message Passing as Reparametrization of Graphical
Models},
booktitle  = {In Proceedings of the 30th Conference on Uncertainty in
Artificial Intelligence (UAI)},
year = {2014},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/MlGlKeUAI14.pdf}
}
M. Mladenov, A. Globerson, and K. Kersting: Efficient lifting of map lp relaxations using k-locality. In Proceedings of the 17th international conference on artificial intelligence and statistics (aistats) in journal of machine learning research (jmlr) workshop & conference proceedings series, 33, 2014. [Bibtex]

@InProceedings{ 14-mladenov-Efficient,
author  = {Mladenov, Martin and Globerson, Amir and Kersting,
Kristian},
title = {Efficient Lifting of MAP LP Relaxations Using k-Locality},
booktitle  = {Proceedings of the 17th International Conference on
Artificial Intelligence and Statistics (AISTATS) in Journal
of Machine Learning Research (JMLR) Workshop & Conference
Proceedings Series},
year = {2014},
volume  = {33},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/MlGlKeAISTATS14.pdf}
}
R. Martín-Martín and O. Brock: Online Interactive Perception of Articulated Objects with Multi-Level Recursive Estimation Based on Task-Specific Priors. In Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2014. [Bibtex]

@InProceedings{ 14-martín-martín-Online,
address  = {Chicago, Illinois, USA},
title = {Online {Interactive} {Perception} of {Articulated}
{Objects} with {Multi}-{Level} {Recursive} {Estimation}
{Based} on {Task}-{Specific} {Priors}},
url = {http://www.robotics.tu-berlin.de/fileadmin/fg170/Publikationen_pdf/IROS14_0282_RobertoMM.pdf},
booktitle  = {Proceedings of the 2014 {IEEE}/{RSJ} {International}
{Conference} on {Intelligent} {Robots} and {Systems}
({IROS})},
author  = {Martín-Martín, Roberto and Brock, Oliver},
year = {2014},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/IROS14_0282_RobertoMM.pdf}
}
G. Martius, L. Jahn, H. Hauser, and Verena V.~Hafner: Self-exploration of the stumpy robot with predictive information maximization. In Proc.\ from animals to animats, sab 2014, 8575, 32-42, Springer, 2014. [Bibtex]

@InProceedings{ 14-martius-Self-Exploration,
title = {Self-Exploration of the Stumpy Robot with Predictive
Information Maximization},
author  = {Georg Martius and Luisa Jahn and Helmut Hauser and Verena
V.~Hafner},
booktitle  = {Proc.\ From Animals to Animats, SAB 2014},
year = {2014},
publisher  = {Springer},
series  = {LNCS},
volume  = {8575},
pages = {32-42},
editor  = {del Pobil, AngelP. and Chinellato, Eris and
Martinez-Martin, Ester and Hallam, John and Cervera, Enric
and Morales, Antonio},
keywords  = {Self-exploration; intrinsic motivation; robot control;
information theory; dynamical systems; learning},
file = {MartiusJahnHauserHafner2014:Stumpy.pdf:http\://robot.informatik.uni-leipzig.de/research/publications/2014/MartiusJahnHauserHafner2014:Stumpy.pdf:PDF},
pdf = {http://robot.informatik.uni-leipzig.de/research/publications/2014/MartiusJahnHauserHafner2014:Stumpy.pdf},
comment  = {best paper award}
}
G. Martius, R. Der, and M. J. Herrmann: Robot learning by guided self-organization. In Guided self-organization: inception, 9, 223-260, Springer Berlin Heidelberg, 2014. [Bibtex]

@InCollection{ 14-martius-Robot,
title = {Robot Learning by Guided Self-Organization},
author  = {Martius, Georg and Der, Ralf and Herrmann, J. Michael},
booktitle  = {Guided Self-Organization: Inception},
publisher  = {Springer Berlin Heidelberg},
year = {2014},
editor  = {Prokopenko, Mikhail},
pages = {223-260},
series  = {Emergence, Complexity and Computation},
volume  = {9},
doi = {10.1007/978-3-642-53734-9_8},
isbn = {978-3-642-53733-2},
url = {http://dx.doi.org/10.1007/978-3-642-53734-9_8}
}
R. Martín-Martín and O. Brock: Online interactive perception of articulated objects with multi-level recursive estimation based on task-specific priors. In Proceedings of the ieee/rsj international conference on intelligent robots and systems, 2494-2501, 2014. [Bibtex]

@InProceedings{ 14-martin-martin-Online,
title = {Online Interactive Perception of Articulated Objects with
Multi-Level Recursive Estimation Based on Task-Specific
Priors},
author  = {Roberto {Mart{\'i}n-Mart{\'i}n} and Oliver Brock},
booktitle  = {Proceedings of the IEEE/RSJ International Conference on
Intelligent Robots and Systems},
pages = {2494-2501},
year = {2014},
location  = {Chicago, Illinois, USA},
pdf = {http://www.robotics.tu-berlin.de/fileadmin/fg170/Publikationen\_pdf/martinmartin\_14\_ip\_iros.pdf},
url2 = {http://ieeexplore.ieee.org/document/6942902/},
projectname  = {Interactive Perception}
}
Q. Li, R. Haschke, and H. Ritter: Object exploration by visuo-tactile servoing. http://www.icub.org/other/icdl-epirob-2014/submissions/li.pdf, In Icdl2014 ws:development of body representations in humans and robots, IEEE, 2014. [Bibtex]

@Conference{ 14-li-Object,
title = {Object exploration by visuo-tactile servoing},
booktitle  = {ICDL2014 WS:Development of body representations in humans
and robots},
howpublished  = {http://www.icub.org/other/icdl-epirob-2014/submissions/li.pdf},
year = {2014},
month = {13/10/2014},
publisher  = {IEEE},
organization  = {IEEE},
address  = {Genoa, Italy},
author  = {Qiang Li and Robert Haschke and Helge Ritter},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/object-exploration-by-visuo-tactile-servoing.pdf}
}
M. Kümmerer, L. Theis, and Matthias Bethge: Deep gaze I: boosting saliency prediction with feature maps trained on imagenet. Corr, abs/1411.1045, 2014. [Bibtex]

@Article{ 14-kummerer-Deep,
author  = {Matthias K{\"{u}}mmerer and Lucas Theis and Matthias
Bethge},
title = {Deep Gaze {I:} Boosting Saliency Prediction with Feature
Maps Trained on ImageNet},
journal  = {CoRR},
volume  = {abs/1411.1045},
year = {2014},
url = {http://arxiv.org/abs/1411.1045},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/1411.1045v2.pdf}
}
J. Kulick, R. Lieck, and J. Kulick: Active learning of hyperparameters: an expected cross entropy criterion for active model selection. e-Print arXiv:1409.7552, 2014. [Bibtex]

@Misc{ 14-kulick-Active,
key = "MaxCE",
author  = "Johannes Kulick and Robert Lieck and Johannes Kulick",
year = "2014",
title = {Active Learning of Hyperparameters: An Expected Cross
Entropy Criterion for Active Model Selection},
howpublished  = "e-Print arXiv:1409.7552",
url = {http://www.arXiv.org/abs/1409.7552},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/1409.7552v1.pdf}
}
K. Kersting, M. Mladenov, R. Garnett, and M. Grohe: Power iterated color refinement. In Proceedings of the twenty-eighth aaai conference on artificial intelligence (aaai-14), AAAI Press, 2014. [Bibtex]

@InProceedings{ 14-kersting-Power,
author  = {Kersting, Kristian and Mladenov, Martin and Garnett, Roman
and Grohe, Martin},
title = {Power Iterated Color Refinement},
booktitle  = {Proceedings of the Twenty-Eighth AAAI Conference on
Artificial Intelligence (AAAI-14)},
year = {2014},
editor  = {Carla Brodley and Peter Stone},
publisher  = {AAAI Press},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/KeMlGaGr14.pdf}
}
S. M. Kazemi, Buchman David, K. Kersting, S. Natarajan, and D. Poole: Relational logistic regression. In In proceedings of the international conference on principles of knowledge representation and reasoning (kr), 2014. [Bibtex]

@InProceedings{ 14-kazemi-Relational,
author  = {Kazemi, Seyed Mehran and Buchman, David, and Kersting,
Kristian and Natarajan, Sriraam and Poole, David},
title = {Relational Logistic Regression},
booktitle  = {In Proceedings of the International Conference on
Principles of Knowledge Representation and Reasoning (KR)},
year = {2014},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/relational_logistic_regression.pdf}
}
M. Jänicke, B. Sick, P. Lukowicz, and D. Bannach: Self-adapting multi-sensor systems: a concept for self-improvement and self-healing techniques. In Self-adaptive and self-organizing systems workshops (sasow), 2014 ieee eighth international conference on, 128-136, 2014. [Bibtex]

@InProceedings{ 14-jänicke-Self-Adapting,
author  = {Jänicke, M. and Sick, B. and Lukowicz, P. and Bannach,
D.},
booktitle  = {Self-Adaptive and Self-Organizing Systems Workshops
(SASOW), 2014 IEEE Eighth International Conference on},
title = {Self-Adapting Multi-sensor Systems: A Concept for
Self-Improvement and Self-Healing Techniques},
year = {2014},
pages = {128-136},
keywords  = {health care;mobile computing;probability;sensor
fusion;activity recognition system;medical care;organic
computing principle;probabilistic model;self-adapting
classification system;self-adapting multisensor
system;self-healing technique;self-improvement
technique;Adaptation models;Intelligent
sensors;Monitoring;Sensor phenomena and
characterization;Sensor systems;Training data},
doi = {10.1109/SASOW.2014.22},
month = {Sept},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/15-sick-paper.pdf}
}
J. Heymann, O. Walter, R. Haeb-Umbach, and B. Raj: Iterative bayesian word segmentation for unspuervised vocabulary discovery from phoneme lattices. In 39th international conference on acoustics, speech and signal processing (icassp 2014), 2014. [Bibtex]

@InProceedings{ 14-heymann-Iterative,
title = {Iterative Bayesian Word Segmentation for Unspuervised
Vocabulary Discovery from Phoneme Lattices},
author  = {Heymann, Jahn and Walter, Oliver and Haeb-Umbach, Reinhold
and Raj, Bhiksha},
booktitle  = {39th International Conference on Acoustics, Speech and
Signal Processing (ICASSP 2014)},
year = { 2014 },
month = {may},
abstract  = { "In this paper we present an algorithm for the
unsupervised segmentation of a lattice produced by a
phoneme recognizer into words. Using a lattice rather than
a single phoneme string accounts for the uncertainty of the
recognizer about the true label sequence. An example
application is the discovery of lexical units from the
output of an error-prone phoneme recognizer in a
zero-resource setting, where neither the lexicon nor the
language model (LM) is known. We propose a computationally
efficient iterative approach, which alternates between the
following two steps: First, the most probable string is
extracted from the lattice using a phoneme LM learned on
the segmentation result of the previous iteration. Second,
word segmentation is performed on the extracted string
using a word and phoneme LM which is learned alongside the
new segmentation. We present results on lattices produced
by a phoneme recognizer on the WSJCAM0 dataset. We show
that our approach delivers superior segmentation
performance than an earlier approach found in the
literature, in particular for higher-order language models.
" },
comment  = {[Poster]},
url = {http://nt.uni-paderborn.de/public/pubs/2014/HeWaHa2014.pdf},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/HeWaHa2014.pdf}
}
S. Gross, B. Mokbel, B. Hammer, and N. Pinkwart: How to select an example? a comparison of selection strategies in example-based learning. In Intelligent tutoring systems, 8474, 340-347, Springer International Publishing, 2014. [Bibtex]

@InProceedings{ 14-gross-How,
title = {How to Select an Example? A Comparison of Selection
Strategies in Example-Based Learning},
author  = {Gross, Sebastian and Mokbel, Bassam and Hammer, Barbara
and Pinkwart, Niels},
booktitle  = {Intelligent Tutoring Systems},
publisher  = {Springer International Publishing},
year = {2014},
editor  = {Trausan-Matu, Stefan and Boyer, KristyElizabeth and
Crosby, Martha and Panourgia, Kitty},
pages = {340-347},
series  = {Lecture Notes in Computer Science},
volume  = {8474},
doi = {10.1007/978-3-319-07221-0_42},
isbn = {978-3-319-07220-3},
keywords  = {intelligent tutoring system; example-based learning;
programming},
language  = {English},
url = {http://dx.doi.org/10.1007/978-3-319-07221-0_42}
}
S. Gross, B. Mokbel, B. Paassen, B. Hammer, and N. Pinkwart: Example-based feedback provision using structured solution spaces. Int. j. of learning technology, 9, 248-280, 2014. [Bibtex]

@Article{ 14-gross-Example-based,
author  = {Gross, S. and Mokbel, B. and Paassen, B. and Hammer, B.
and Pinkwart, N.},
title = {Example-based feedback provision using structured solution
spaces},
editor  = {Uden, L.},
journal  = {Int. J. of Learning Technology},
volume  = {9},
year = {2014},
number  = {3},
pages = {248-280},
doi = {10.1504/IJLT.2014.065752}
}
M. Grohe, K. Kersting, M. Mladenov, and E. Selman: Dimension reduction via colour refinement. In Proceedings of the 22nd european symposium on algorithms (esa), 2014. [Bibtex]

@InProceedings{ 14-grohe-Dimension,
year = {2014},
booktitle  = {Proceedings of the 22nd European Symposium on Algorithms
(ESA)},
title = {Dimension Reduction via Colour Refinement},
author  = { Grohe, Martin and Kersting, Kristian and Mladenov, Martin
and Selman, Erkal},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/GrKeMlSe14.pdf}
}

2013

K. Zahedi and N. Ay: Quantifying morphological computation. Entropy, 15, 1887–1915, 2013. [Bibtex]

@Article{ 13-zahedi-Quantifying,
author  = {Zahedi, Keyan and Ay, Nihat},
journal  = {Entropy},
number  = {5},
pages = {1887--1915},
title = {Quantifying Morphological Computation},
volume  = {15},
year = {2013}
}
C. Wirth and J. Fürnkranz: Preference-based reinforcement learning: a preliminary survey. In Proceedings of the ecml/pkdd-13 workshop on reinforcement learning from generalized feedback: beyond numeric rewards, 2013. [Bibtex]

@InProceedings{ 13-wirth-Preference-Based,
author  = {Wirth, Christian and F{\"{u}}rnkranz, Johannes},
editor  = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
title = {Preference-Based Reinforcement Learning: A Preliminary
Survey},
booktitle  = {Proceedings of the ECML/PKDD-13 Workshop on Reinforcement
Learning from Generalized Feedback: Beyond Numeric
Rewards},
year = {2013},
pdf = {http://www.ke.tu-darmstadt.de/events/PBRL-13/papers/10-Wirth.pdf}
}
C. Wirth and J. Fürnkranz: A policy iteration algorithm for learning from preference-based feedback. In Advances in intelligent data analysis xii: 12th international symposium (ida-13), 8207, Springer-Verlag, 2013. [Bibtex]

@InProceedings{ 13-wirth-Policy,
author  = {Wirth, Christian and F{\"{u}}rnkranz, Johannes},
editor  = {Tucker, Allan and H{\"{o}}ppner, Frank and Siebes, Arno
and Swift, Stephen},
month = oct,
title = {A Policy Iteration Algorithm for Learning from
Preference-based Feedback},
booktitle  = {Advances in Intelligent Data Analysis XII: 12th
International Symposium (IDA-13)},
series  = {LNCS},
volume  = {8207},
year = {2013},
publisher  = {Springer-Verlag}
}
C. Wirth and J. Fürnkranz: Learning from trajectory-based action preferences. In Proceedings of the icra 2013 workshop on autonomous learning, To be published, 2013. [Bibtex]

@InProceedings{ 13-wirth-Learning,
author  = {Wirth, Christian and F{\"{u}}rnkranz, Johannes},
month = may,
title = {Learning from Trajectory-Based Action Preferences},
booktitle  = {Proceedings of the ICRA 2013 Workshop on Autonomous
Learning},
year = {2013},
location  = {Karslruhe},
note = {To be published}
}
C. Wirth and J. Fürnkranz: Epmc: Every visit preference Monte Carlo for reinforcement learning. In Proceedings of the 5th asian conference on machine learning, (acml-13), 29, 483–497, JMLR.org, 2013. [Bibtex]

@InProceedings{ 13-wirth-EPMC,
author  = {Wirth, Christian and F{\"{u}}rnkranz, Johannes},
editor  = {Ong, Cheng Soon and Ho, Tu-Bao},
title = {EPMC: {Every} Visit Preference {Monte Carlo} for
Reinforcement Learning},
booktitle  = {Proceedings of the 5th Asian Conference on Machine
Learning, (ACML-13)},
series  = {JMLR Proceedings},
volume  = {29},
year = {2013},
pages = {483--497},
publisher  = {JMLR.org},
address  = {Canberra, ACT, Australia},
pdf = {http://jmlr.org/proceedings/papers/v29/Wirth13.html}
}
P. Weng, R. Busa-Fekete, and E. Hüllermeier: Interactive Q-learning with ordinal rewards and unreliable tutor. In Proceedings ecml/pkdd workshop on reinforcement learning from generalized feedback: beyond numerical rewards, 2013. [Bibtex]

@InCollection{ 13-weng-Interactive,
author  = {Weng, Paul and Busa-Fekete, Robert and H{\"{u}}llermeier,
Eyke},
title = {Interactive {Q}-Learning with Ordinal Rewards and
Unreliable Tutor},
booktitle  = {Proceedings ECML/PKDD Workshop on Reinforcement learning
from Generalized Feedback: Beyond Numerical Rewards},
year = {2013},
address  = {Prague}
}
O. Walter, R. Haeb-Umbach, Sourish Chaudhuri, and B. Raj: Unsupervised word discovery from phonetic input using nested pitman-yor language modeling. ICRA Workshop on Autonomous Learning, 2013. [Bibtex]

@Misc{ 13-walter-Unsupervised,
author  = "Oliver Walter and Reinhold Haeb-Umbach and Sourish
Chaudhuri and Bhiksha Raj",
title = "Unsupervised Word Discovery from Phonetic Input Using
Nested Pitman-Yor Language Modeling",
year = "2013",
abstract  = "In this paper we consider the unsupervised word discovery
from phonetic input. We employ a word segmentation
algorithm which simultaneously develops a lexicon, i.e.,
the transcription of a word in terms of a phone sequence,
learns a n-gram language model describing word and word
sequence probabilities, and carries out the segmentation
itself. The underlying statistical model is that of a
Pitman-Yor process, a concept known from Bayesian
non-parametrics, which allows for an a priori unknown and
unlimited number of different words. Using a hierarchy of
Pitman-Yor processes, language models of different order
can be employed and nesting it with another hierarchy of
Pitman-Yor processes on the phone level allows for backing
off unknown word unigrams by phone m-grams. We present
results on a large-vocabulary task, assuming an error-free
phone sequence is given. We finish by discussing options
how to cope with noisy phone sequences.",
howpublished  = "ICRA Workshop on Autonomous Learning",
pdf = {../wp-content/papercite-data/pdfs/2013 Haeb-Umbach 4
Unsupervised_Word_Discovery_from_Phonetic_Input_Using_Nested_Pitman-Yor_Language_Modeling.pdf}
}
O. Walter, J. Schmalenstroer, and Reinhold Haeb-Umbach: A novel initialization method for unsupervised learning of acoustic patterns in speech. 2013. [Bibtex]

@TechReport{ 13-walter-Novel,
title = "A Novel Initialization Method for Unsupervised Learning of
Acoustic Patterns in Speech",
author  = "Oliver Walter and Joerg Schmalenstroer and Reinhold
Haeb-Umbach",
institution  = "Department of Communications Engineering",
number  = "FGNT-2013-01",
year = "2013",
abstract  = "In this paper we present a novel initialization method for
unsupervised learning of acoustic patterns in recordings of
continuous speech. The pattern discovery task is solved by
dynamic time warping whose performance we improve by a
smart starting point selection. This enables a more
accurate discovery of patterns compared to conventional
approaches. After graph-based clustering the patterns are
employed for training hidden Markov models for an
unsupervised speech acquisition. By iterating between model
training and decoding in an EM-like framework the word
accuracy is continuously improved. On the TIDIGITS corpus
we achieve a word error rate of about 13\% by the proposed
unsupervised pattern discovery approach, which neither
assumes knowledge of the acoustic units nor of the labels
of the training data.",
pdf = {../wp-content/papercite-data/pdfs/2013 Haeb-Umbach 3
A_Novel_Initialization_Method_For_Unsupervised_Learning_Of_Acoustic_Patterns_in_Speech_TR.pdf}
}
O. Walter, T. Korthals, R. Haeb-Umbach, and B. Raj: A hierarchical system for word discovery exploiting dtw-based initialization. In Proc. asru, 2013. [Bibtex]

@InProceedings{ 13-walter-Hierarchical,
author  = "Oliver Walter and Timo Korthals and Reinhold Haeb-Umbach
and Bhiksha Raj",
title = "A Hierarchical System for Word Discovery Exploiting
DTW-Based Initialization",
booktitle  = "Proc. ASRU",
address  = "Olomouc, Czech Republic",
year = "2013",
abstract  = "Discovering the linguistic structure of a language solely
from spoken input asks for two steps: phonetic and lexical
discovery. The first is concerned with identifying the
categorical subword unit inventory and relating it to the
underlying acoustics, while the second aims at discovering
words as repeated patterns of subword units. The
hierarchical approach presented here accounts for
classification errors in the first stage by modelling the
pronunciation of a word in terms of subword units
probabilistically: a hidden Markov model with discrete
emission probabilities, emitting the observed subword unit
sequences. We describe how the system can be learned in a
completely unsupervised fashion from spoken input. To
improve the initialization of the training of the word
pronunciations, the output of a dynamic time warping based
acoustic pattern discovery system is used, as it is able to
discover similar temporal sequences in the input data. This
improved initialization, using only weak supervision, has
led to a 40\% reduction in word error rate on a digit
recognition task.",
pdf = {../wp-content/papercite-data/pdfs/2013 Haeb-Umbach 1
A_HIERARCHICAL_SYSTEM_FOR_WORD_DISCOVERY_EXPLOITING_DTW-BASED_INITIALIZATION.pdf}
}
N. A. Vien and M. Toussaint: Reasoning with uncertainties over existence of objects. AAAI Technical Report FS-13-02, 2013. [Bibtex]

@Misc{ 13-vien-Reasoning,
key = "AAAIws",
year = "2013",
title = "Reasoning with Uncertainties Over Existence Of Objects",
author  = "Ngo Anh Vien and Marc Toussaint",
howpublished  = "AAAI Technical Report FS-13-02",
url = {https://ipvs.informatik.uni-stuttgart.de/mlr/marc/publications/13-vien-AAAIws.pdf},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/13-vien-AAAIws.pdf}
}
M. Toussaint, T. Lang, and N. Jetchev: Kognitive robotik – herausforderungen an unser verständnis natürlicher umgebungen. Automatisierungstechnik, 2013. [Bibtex]

@Article{ 13-toussaint-Kognitive,
key = "AT",
author  = "Marc Toussaint and Tobias Lang and Nikolay Jetchev",
title = "Kognitive Robotik -- Herausforderungen an unser
Verst{\"a}ndnis nat{\"u}rlicher Umgebungen",
journal  = "Automatisierungstechnik",
year = "2013",
url = {https://ipvs.informatik.uni-stuttgart.de/mlr/marc/publications/13-toussaint-AT.pdf},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/13-toussaint-AT.pdf}
}
Werner von and K. Behrend: Prinzipien neuronaler informationsverarbeitung. Manuscript, 2013. [Bibtex]

@Misc{ 13-seelen-Prinzipien,
title = "Prinzipien neuronaler Informationsverarbeitung",
author  = "{Werner von} Seelen and Konstantin Behrend",
howpublished  = "Manuscript",
year = "2013",
pdf = {http://autonomous-learning.org/wp-content/uploads/Paper-vonSeelen.pdf}
}
S. Schulz, S. Ulbrich, T. Asfour, and R. Dillmann: Incremental Construction of Motion Graphs based on Binary SpacePartitioning. In IEEE International Conference on Robotics and Automation (ICRA), 2013. [Bibtex]

@InProceedings{ 13-schulz-Incremental-Construction-of-Motion-Graphs-based-on-Binary-SpacePartitioning,
author  = {S. Schulz and S. Ulbrich and T. Asfour and R. Dillmann},
title = {{Incremental Construction of Motion Graphs based on Binary
SpacePartitioning}},
booktitle  = {{IEEE International Conference on Robotics and Automation
(ICRA)}},
year = {2013}
}
C. W. Rempis, H. Toutounji, and F. Pasemann: Evaluating neuromodulator-controlled stochastic plasticity for learning recurrent neural control networks. In Proceedings of the 5$^{\text{th}}$ international conference on neural computation theory and applications., 2013. [Bibtex]

@InProceedings{ 13-rempis-Evaluating,
author  = {Rempis, Christian W. and Toutounji, Hazem and Pasemann,
Frank},
title = {Evaluating Neuromodulator-Controlled Stochastic Plasticity
for Learning Recurrent Neural Control Networks},
year = {2013},
booktitle  = {Proceedings of the 5$^{\text{th}}$ International
Conference on Neural Computation Theory and Applications.},
pdf = {../wp-content/papercite-data/pdfs/2013 pasemann 2.pdf}
}
C. Rempis, H. Toutounji, and F. Pasemann: Controlling the learning of behaviors in the sensorimotor loop with neuromodulators in self-monitoring neural networks. ICRA Workshop on Autonomous Learning, 2013. [Bibtex]

@Misc{ 13-rempis-Controlling,
author  = {Christian Rempis and Hazem Toutounji and Frank Pasemann},
title = {Controlling the Learning of Behaviors in the Sensorimotor
Loop with Neuromodulators in Self-Monitoring Neural
Networks},
year = {2013},
howpublished  = {ICRA Workshop on Autonomous Learning},
pdf = {../wp-content/papercite-data/pdfs/2013 pasemann 1.pdf}
}
F. Pasemann: Self-regulating neurons in the sensorimotor loop. In Advances in computational intelligence, 7902, 481–491, Springer Berlin Heidelberg, 2013. [Bibtex]

@InCollection{ 13-pasemann-Self-regulating,
title = {Self-regulating Neurons in the Sensorimotor Loop},
author  = {Pasemann, Frank},
year = {2013},
booktitle  = {Advances in Computational Intelligence},
volume  = {7902},
series  = {Lecture Notes in Computer Science},
editor  = {Rojas, Ignacio and Joya, Gonzalo and Gabestany, Joan},
publisher  = {Springer Berlin Heidelberg},
pages = {481--491},
pdf = {../wp-content/papercite-data/pdfs/2013 pasemann 3.pdf}
}
A. Orthey, M. Toussaint, and N. Jetchev: Optimizing motion primitives to make symbolic models more predictive. In Proc. of the ieee int. conf. on robotics and automation (icra 2013), 2013. [Bibtex]

@InProceedings{ 13-orthey-Optimizing,
key = "ICRA",
author  = "Andreas Orthey and Marc Toussaint and Nikolay Jetchev",
title = "Optimizing Motion Primitives to Make Symbolic Models More
Predictive",
booktitle  = icra # " (ICRA 2013)",
year = "2013",
url = {BASEURL/13-orthey-ICRA.pdf},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/13-orthey-ICRA.pdf}
}
M. Missura, C. Müstermann, Malte Mauelshagen, M. Schreiber, and S. Behnke: RoboCup 2012 Best Humanoid Award winner NimbRo TeenSize. In Robocup 2012: robot soccer world cup xvi, Springer, to appear, 2013. [Bibtex]

@InProceedings{ 13-missura-RoboCup,
author  = {Marcell Missura and Cedrick Müstermann and Malte
Mauelshagen and Michael Schreiber and Sven Behnke},
title = {{RoboCup} 2012 {Best} {Humanoid} {Award} Winner {NimbRo}
{TeenSize}},
booktitle  = {RoboCup 2012: Robot Soccer World Cup XVI},
year = {2013},
publisher  = {Springer},
note = {to appear},
pdf = {../wp-content/papercite-data/pdfs/2013Missura.pdf}
}
M. Missura and S. Behnke: Omnidirectional capture steps for bipedal walking. In Ieee-ras int. conf. on humanoid robots (humanoids), 2013. [Bibtex]

@InProceedings{ 13-missura-Omnidirectional,
author  = {Marcell Missura and Sven Behnke},
title = {Omnidirectional Capture Steps for Bipedal Walking},
booktitle  = {IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids)},
year = {2013}
}
G. Martius: Robustness of guided self-organization against sensorimotor disruptions. Advances in complex systems, 16, 1350001, 2013. [Bibtex]

@Article{ 13-martius-Robustness,
author  = {Georg Martius},
journal  = {Advances in Complex Systems},
title = {Robustness of guided self-organization against
sensorimotor disruptions},
year = {2013},
volume  = {16},
pages = {1350001},
number  = {02n03},
doi = {10.1142/S021952591350001X},
pdf = {http://robot.informatik.uni-
leipzig.de/research/publications/2012/martius_robustness_of_gso_12_watermark.pdf},
eprint  = {http://www.worldscientific.com/doi/pdf/10.1142/S021952591350001X}
}
G. Martius, R. Der, and N. Ay: Information driven self-organization of complex robotic behaviors. Plos one, 8, e63400, Public Library of Science, 2013. [Bibtex]

@Article{ 13-martius-Information,
author  = {Georg Martius and Ralf Der and Nihat Ay},
journal  = {PLoS ONE},
publisher  = {Public Library of Science},
title = {Information Driven Self-Organization of Complex Robotic
Behaviors},
year = {2013},
month = {05},
volume  = {8},
pages = {e63400},
number  = {5},
doi = {10.1371/journal.pone.0063400}
}
Q. Li, C. Schürmann, R. Haschke, and H. Ritter: A control framework for tactile servoing. In Rss 2013, 2013. [Bibtex]

@Conference{ 13-li-control,
title = {A control framework for tactile servoing},
booktitle  = {RSS 2013},
year = {2013},
month = {24/06/2013},
address  = {Berlin Germany},
author  = {Qiang Li and Carsten Sch{\"u}rmann and Robert Haschke and
Helge Ritter},
pdf = {../wp-content/papercite-data/pdfs/2013 Bielefeld Ritter
4Qiangrss.pdf}
}
Q. Li, R. Haschke, and H. Ritter: Two-fingered, tactile-based manipulation of unknown objects. In Rss ws: sensitive robotics, 2013. [Bibtex]

@Conference{ 13-li-Two-fingered,
title = {Two-fingered, tactile-based manipulation of unknown
objects},
booktitle  = {RSS WS: Sensitive Robotics},
year = {2013},
month = {June 27},
address  = {Berlin, Germany},
author  = {Qiang Li and Robert Haschke and Helge Ritter},
pdf = {../wp-content/papercite-data/pdfs/2013 Bielefeld Ritter
5Qiangrssws.pdf}
}
Q. Li, R. Haschke, and H. Ritter: Toward autonoumous visual-tactile exploration and manipulation. In Icra2013 ws:interactive perception, IEEE, 2013. [Bibtex]

@Conference{ 13-li-Toward,
title = {Toward Autonoumous Visual-tactile Exploration and
Manipulation},
booktitle  = {ICRA2013 WS:Interactive Perception},
year = {2013},
month = {06/05/2013},
publisher  = {IEEE},
organization  = {IEEE},
address  = {Karlsruhe, Germany},
author  = {Qiang Li and Robert Haschke and Helge Ritter},
pdf = {../wp-content/papercite-data/pdfs/2013 Bielefeld Ritter
1Qiangicra2013ws.pdf}
}
Q. Li, M. Meier, R. Haschke, Bram Bolder, and H. Ritter: Rotary object dexterous manipulation in hand: a feedback-based method. International journal of mechatronics and automation (ijma), Vol.3, 12, 2013. [Bibtex]

@Article{ 13-li-Rotary,
title = {Rotary Object Dexterous Manipulation in Hand: A
Feedback-based Method},
journal  = {International Journal of Mechatronics and Automation
(IJMA)},
volume  = {Vol.3},
year = {2013},
pages = {12},
chapter  = {36},
author  = {Qiang Li and Martin Meier and Robert Haschke and Bram
Bolder and Helge Ritter},
pdf = {../wp-content/papercite-data/pdfs/2013 Bielefeld Ritter
2QiangIJMA.pdf}
}
Q. Li, C. Elbrechter, R. Haschke, and H. Ritter: Integrating vision, haptics and proprioception into a feedback controller for in-hand manipulation of unknown objects. Iros2013, IEEE, 2013. [Bibtex]

@InProceedings{ 13-li-Integrating,
title = {Integrating vision, haptics and proprioception into a
feedback controller for in-hand manipulation of unknown
objects},
journal  = {IROS2013},
year = {2013},
month = {November 3.},
publisher  = {IEEE},
address  = {Tokyo, Japan},
author  = {Qiang Li and Christof Elbrechter and Robert Haschke and
Helge Ritter},
pdf = {../wp-content/papercite-data/pdfs/2013 Bielefeld Ritter
3Qiangiros.pdf}
}
T. Lang, M. Toussaint, and K. Kersting: Exploration in relational domains for model-based reinforcement learning. Journal of machine learning research, 2013. [Bibtex]

@Article{ 13-lang-Exploration,
author  = {Tobias Lang and Marc Toussaint and Kristian Kersting},
title = {Exploration in Relational Domains for Model-based
Reinforcement Learning},
journal  = {Journal of Machine Learning Research},
year = {2013},
pdf = {../wp-content/papercite-data/pdfs/13-lang-toussaint-kersting-JMLR.pdf}
}
J. Kulick, M. Toussaint, T. Lang, and M. Lopes: Active learning for teaching a robot grounded relational symbols. In Proc. of the int. joint conf. on artificial intelligence (ijcai 2013), 2013. [Bibtex]

@InProceedings{ 13-kulick-Active,
key = "IJCAI",
year = "2013",
title = "Active Learning for Teaching a Robot Grounded Relational
Symbols",
author  = "Johannes Kulick and Marc Toussaint and Tobias Lang and
Manuel Lopes",
booktitle  = ijcai # " (IJCAI 2013)",
url = {https://ipvs.informatik.uni-stuttgart.de/mlr/marc/publications/13-kulick-IJCAI.pdf},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/13-kulick-IJCAI.pdf}
}
N. Jetchev, T. Lang, and M. Toussaint: Learning grounded relational symbols from continuous data for abstract reasoning. ICRA Workshop on Autonomous Learning, 2013. [Bibtex]

@Misc{ 13-jetchev-Learning,
key = "ICRAws",
year = "2013",
title = "Learning Grounded Relational Symbols from Continuous Data
for Abstract Reasoning",
author  = "Nikolay Jetchev and Tobias Lang and Marc Toussaint",
howpublished  = "ICRA Workshop on Autonomous Learning",
url = {http://ipvs.informatik.uni-stuttgart.de/mlr/marc/publications/13-jetchev-ICRAws.pdf},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/13-jetchev-ICRAws.pdf}
}
E. Hüllermeier and J. Fürnkranz: Editorial: preference learning and ranking. Machine learning, 93, 185–189, 2013. [Bibtex]

@Article{ 13-hullermeier-Editorial,
author  = {H{\"{u}}llermeier, Eyke and F{\"{u}}rnkranz, Johannes},
title = {Editorial: Preference Learning and Ranking},
journal  = {Machine Learning},
volume  = {93},
number  = {2-3},
year = {2013},
pages = {185--189},
pdf = {http://www.springer.com/alert/urltracking.do?id=L35fd080Md1c8edSad2177d}
}
J. Heymann, O. Walter, R. Haeb-Umbach, and B. Raj: Unsupervised word segmentation from noisy input. In Proc. asru, 2013. [Bibtex]

@InProceedings{ 13-heymann-Unsupervised,
title = "Unsupervised Word Segmentation from Noisy Input",
booktitle  = "Proc. ASRU",
author  = "Jahn Heymann and Oliver Walter and Reinhold Haeb-Umbach
and Bhiksha Raj",
address  = "Olomouc, Czech Republic",
year = "2013",
abstract  = "In this paper we present an algorithm for the unsupervised
segmentation of a character or phoneme lattice into words.
Using a lattice at the input rather than a single string
accounts for the uncertainty of the character/phoneme
recognizer about the true label sequence. An example
application is the discovery of lexical units from the
output of an error-prone phoneme recognizer in a
zero-resource setting, where neither the lexicon nor the
language model is known. Recently a Weighted Finite State
Transducer (WFST) based approach has been published which
we show to suffer from an issue: language model
probabilities of known words are computed incorrectly.
Fixing this issue leads to greatly improved precision and
recall rates, however at the cost of increased
computational complexity. It is therefore practical only
for single input strings. To allow for a lattice input and
thus for errors in the character/phoneme recognizer, we
propose a computationally efficient suboptimal two-stage
approach, which is shown to significantly improve the word
segmentation performance compared to the earlier WFST
approach.",
pdf = {../wp-content/papercite-data/pdfs/2013 Haeb-Umbach 2
UNSUPERVISED_WORD_SEGMENTATION_FROM_NOISY_INPUT.pdf}
}
F. Hadiji and K. Kersting: Reduce and re–lift: bootstrapped lifted likelihood maximization for map. In In proceedings of the twenty-seventh aaai conference on artificial intelligence (aaai-13), 2013. [Bibtex]

@InProceedings{ 13-hadiji-Reduce,
author  = {F. Hadiji and K. Kersting},
title = {Reduce and Re{--}Lift: Bootstrapped Lifted Likelihood
Maximization for MAP},
year = {2013},
booktitle  = {In Proceedings of the Twenty-Seventh AAAI Conference on
Artificial Intelligence (AAAI-13) },
pdf = {../wp-content/papercite-data/pdfs/2013 Kersting 3
hadiji13aaai.pdf}
}
S. Gross, B. Mokbel, B. Hammer, and N. Pinkwart: Towards a domain-independent its middleware architecture. In Proceedings of the 13th ieee international conference on advanced learning technologies (icalt), 408-409, 2013. [Bibtex]

@InProceedings{ 13-gross-Towards,
year = {2013},
booktitle  = {Proceedings of the 13th IEEE International Conference on
Advanced Learning Technologies (ICALT)},
editor  = {Chen, N.-S. and Huang, R. and Kinshuk and Li, Y. and
Sampson, D. G.},
author  = {Gross, S. and Mokbel, B. and Hammer, B. and Pinkwart, N.},
title = {Towards a Domain-Independent ITS Middleware Architecture},
pages = {408 - 409},
keywords  = {intelligent tutoring system, middleware, machine
learning},
doi = {10.1109/ICALT.2013.124},
pdf = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6601966}
}
D. Fierens, K. Kersting, J. Davis, J. Chen, and M. Mladenov: Pairwise markov logic. In Postproceedings of the 22nd international conference on inductive logic programming (ilp–2012), Springer, 2013. [Bibtex]

@InProceedings{ 13-fierens-Pairwise,
author  = {D. Fierens and K. Kersting and J. Davis and J. Chen and M.
Mladenov},
title = {Pairwise Markov Logic},
year = {2013},
booktitle  = {Postproceedings of the 22nd International Conference on
Inductive Logic Programming (ILP{--}2012)},
editor  = {F. Riguzzi, F. Zelezny},
publisher  = {Springer},
series  = {LNCS},
pdf = {../wp-content/papercite-data/pdfs/2013 Kersting
2fierens12ilp_pairwise.pdf}
}
R. Der and G. Martius: Behavior as broken symmetry in embodied self- organizing robots. In Advances in artificial life, ecal 2013, 601-608, MIT Press, 2013. [Bibtex]

@InCollection{ 13-der-Behavior,
title = {Behavior as broken symmetry in embodied self- organizing
robots},
author  = {Ralf Der and Georg Martius},
booktitle  = {Advances in Artificial Life, ECAL 2013},
publisher  = {MIT Press},
year = {2013},
pages = {601-608},
affiliation  = {Max Planck Institute for Mathematics in the Sciences},
pdf = {http://robot.informatik.uni-leipzig.de/research/publications/2013/ECAL2013.pdf}
}
R. Busa-Fekete, B. Szörényi, P. Weng, W. Cheng, and E. Hüllermeier: Top-k selection based on adaptive sampling of noisy preferences. In Proceedings of the international conference on machine learning (icml-13), 2013. [Bibtex]

@InCollection{ 13-busa-fekete-Top-k,
author  = {Busa-Fekete, Robert and Sz{\"{o}}r{\'{e}}nyi, Balazs and
Weng, Paul and Cheng, Weiwei and H{\"{u}}llermeier, Eyke},
title = {Top-k Selection based on Adaptive Sampling of Noisy
Preferences},
booktitle  = {Proceedings of the International Conference on Machine
Learning (ICML-13)},
year = {2013},
address  = {Atlanta, USA}
}
R. Busa-Fekete, B. Szörényi, P. Weng, and E. Hüllermeier: Preference-based evolutionary direct policy search. In Proceedings of the ecml/pkdd workshop on reinforcement learning from generalized feedback: beyond numerical rewards, 2013. [Bibtex]

@InCollection{ 13-busa-fekete-Preference-based,
author  = {Busa-Fekete, Robert and Sz{\"{o}}r{\'{e}}nyi, Balazs and
Weng, Paul and H{\"{u}}llermeier, Eyke},
title = {Preference-based Evolutionary Direct Policy Search},
booktitle  = {Proceedings of the ECML/PKDD Workshop on Reinforcement
learning from Generalized Feedback: Beyond Numerical
Rewards},
year = {2013},
address  = {Prague}
}
W. Böhmer and K. Obermayer: Towards structural generalization: factored approximate planning. ICRA Workshop on Autonomous Learning, 2013. [Bibtex]

@Misc{ 13-bohmer-Towards,
author  = {Wendelin B\"ohmer and Klaus Obermayer},
title = {Towards Structural Generalization: Factored Approximate
Planning},
year = {2013},
howpublished  = {ICRA Workshop on Autonomous Learning},
url = {http://autonomous-learning.org/wp-content/uploads/13-ALW/paper_1.pdf}
}
W. Böhmer, S. Grünewälder, Y. Shen, M. Musial, and K. Obermayer: Construction of approximation spaces for reinforcement learning. Journal of machine learning research, 14, 2067–2118, 2013. [Bibtex]

@Article{ 13-bohmer-Construction,
title = {Construction of Approximation Spaces for Reinforcement
Learning},
author  = {Wendelin B\"ohmer and Steffen Gr\"unew\"alder and Yun Shen
and Marek Musial and Klaus Obermayer},
pages = {2067--2118},
year = {2013},
journal  = {Journal of Machine Learning Research},
volume  = {14},
month = {July},
abstract  = {Linear reinforcement learning (RL) algorithms like
least-squares temporal difference learning (LSTD) require
basis functions that span approximation spaces of potential
value functions. This article investigates methods to
construct these bases from samples. We hypothesize that an
ideal approximation spaces should encode diffusion
distances and that slow feature analysis (SFA) constructs
such spaces. To validate our hypothesis we provide
theoretical statements about the LSTD value approximation
error and induced metric of approximation spaces
constructed by SFA and the state-of-the-art methods Krylov
bases and proto-value functions (PVF). In particular, we
prove that SFA minimizes the average (over all tasks in the
same environment) bound on the above approximation error.
Compared to other methods, SFA is very sensitive to
sampling and can sometimes fail to encode the whole state
space. We derive a novel importance sampling modification
to compensate for this effect. Finally, the LSTD and least
squares policy iteration (LSPI) performance of
approximation spaces constructed by Krylov bases, PVF, SFA
and PCA is compared in benchmark tasks and a visual robot
navigation experiment (both in a realistic simulation and
with a robot). The results support our hypothesis and
suggest that (i) SFA provides subspace-invariant features
for MDPs with self-adjoint transition operators, which
allows strong guarantees on the approximation error, (ii)
the modified SFA algorithm is best suited for LSPI in both
discrete and continuous state spaces and (iii)
approximation spaces encoding diffusion distances
facilitate LSPI performance.},
pdf = {../wp-content/papercite-data/pdfs/Boehmer13 - Construction
of Approximation Spaces for Reinforcement Learning.pdf}
}
N. Ay and K. Zahedi: An information theoretic approach to intention and deliberative decision-making of embodied systems. In Advances in cognitive neurodynamics iii, Springer, 2013. [Bibtex]

@InCollection{ 13-ay-Information,
address  = {Heidelberg},
author  = {Ay, Nihat and Zahedi, Keyan},
booktitle  = {Advances in cognitive neurodynamics III},
publisher  = {Springer},
title = {An Information Theoretic Approach to Intention and
Deliberative Decision-Making of Embodied Systems},
year = {2013}
}
B. Ahmadi, K. Kersting, M. Mladenov, and S. Natarajan: Exploiting symmetries for scaling loopy belief propagation and relational training. Machine learning journal, 92, 91{–}132, 2013. [Bibtex]

@Article{ 13-ahmadi-Exploiting,
author  = {B. Ahmadi and K. Kersting and M. Mladenov and S.
Natarajan},
title = {Exploiting Symmetries for Scaling Loopy Belief Propagation
and Relational Training},
journal  = {Machine Learning Journal},
year = {2013},
volume  = {92},
number  = {1},
pages = {91{--}132},
month = {July},
pdf = {../wp-content/papercite-data/pdfs/2013 Kersting
1ahmadi13mlj.pdf}
}

2012

C. Wirth and J. Fürnkranz: First steps towards learning from game annotations. In Workshop proceedings – preference learning: problems and applications in ai at ecai 2012, 53-58, 2012. [Bibtex]

@InProceedings{ 12-wirth-First,
author  = {Wirth, Christian and F{\"{u}}rnkranz, Johannes},
month = aug,
title = {First Steps Towards Learning from Game Annotations},
booktitle  = {Workshop Proceedings - Preference Learning: Problems and
Applications in AI at ECAI 2012},
year = {2012},
pages = {53-58},
location  = {Montpellier},
pdf = {http://www.ke.tu-darmstadt.de/events/PL-12/papers/11-wirth.pdf}
}
S. Ulbrich, M. Bechtel, T. Asfour, and R. Dillmann: Learning robot dynamics with kinematic bézier maps. In Ieee/rsj international conference on intelligent robots and systems (iros), 2012. [Bibtex]

@InProceedings{ 12-ulbrich-Learning,
author  = {S. Ulbrich and M. Bechtel and T. Asfour and R. Dillmann},
title = {Learning Robot Dynamics with Kinematic Bézier Maps},
booktitle  = {IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS)},
year = {2012},
pdf = {http://his.anthropomatik.kit.edu/pdf_humanoids/Ulbrich2012c.pdf}
}
S. Ulbrich, V. Ruiz, T. Asfour, C. Torras, and R. Dillmann: Kinematic bézier maps. Ieee transactions on systems, man, and cybernetics, 42, 1215-1230, 2012. [Bibtex]

@Article{ 12-ulbrich-Kinematic,
author  = {S. Ulbrich and V. Ruiz and T. Asfour and C. Torras and R.
Dillmann},
title = {Kinematic Bézier Maps},
journal  = {IEEE Transactions on Systems, Man, and Cybernetics},
year = {2012},
volume  = {42},
pages = {1215-1230},
number  = {4},
pdf = {http://his.anthropomatik.kit.edu/pdf_humanoids/Ulbrich2012b.pdf}
}
S. Ulbrich, V. Ruiz, T. Asfour, C. Torras, and R. Dillmann: General kinematics decomposition without intermediate markers. Ieee transactions on neural networks and learning systems, 23, 620-630, 2012. [Bibtex]

@Article{ 12-ulbrich-General,
author  = {S. Ulbrich and V. Ruiz and T. Asfour and C. Torras and R.
Dillmann},
title = {General Kinematics Decomposition without Intermediate
Markers},
journal  = {IEEE Transactions on Neural Networks and Learning
Systems},
year = {2012},
volume  = {23},
pages = {620-630},
number  = {4},
pdf = {http://his.anthropomatik.kit.edu/pdf_humanoids/Ulbrich2012a.pdf}
}
L. Theis, J. Sohl-Dickstein, and M. Bethge: Training sparse natural image models with a fast Gibbs sampler of an extended state space. In Advances in neural information processing systems 25, 2012. [Bibtex]

@InProceedings{ 12-theis-Training-sparse-natural-image-models-with-a-fast-Gibbs-sampler-of-an-extended-state-space,
author  = {L. Theis and J. Sohl-Dickstein and M. Bethge},
title = {{Training sparse natural image models with a fast Gibbs
sampler of an extended state space}},
booktitle  = {Advances in Neural Information Processing Systems 25},
year = {2012},
month = {Nov},
keywords  = {natural image statistics, ica, overcompleteness},
pdf = {http://books.nips.cc/papers/files/nips25/NIPS2012_0540.pdf}
}
L. Theis, R. Hosseini, and M. Bethge: Mixtures of conditional gaussian scale mixtures applied to multiscale image representations. Plos one, 7, Public Library of Science, 2012. [Bibtex]

@Article{ 12-theis-Mixtures,
author  = {L. Theis and R. Hosseini and M. Bethge},
title = {Mixtures of Conditional Gaussian Scale Mixtures Applied to
Multiscale Image Representations},
journal  = {PLoS ONE},
year = {2012},
volume  = {7},
number  = {7},
month = {Jul},
doi = {10.1371/journal.pone.0039857},
keywords  = {natural image statistics, gaussian scale mixtures, random
fields},
publisher  = {Public Library of Science},
pdf = {http://autonomous-learning.org/wp-content/uploads/theis12a.pdf}
}
S. Schneegans and G. Schöner: A neural mechanism for coordinate transformation predicts pre-saccadic remapping. Biological cybernetics, 1–21, Springer, 2012. [Bibtex]

@Article{ 12-schneegans-neural,
author  = {Schneegans, S. and Sch{\"o}ner, G.},
title = {A neural mechanism for coordinate transformation predicts
pre-saccadic remapping},
journal  = {Biological cybernetics},
year = {2012},
pages = {1--21},
publisher  = {Springer},
pdf = {http://autonomous-learning.org/wp-content/uploads/schoener.pdf}
}
W. Samek, K. Müller, M. Kawanabe, and C. Vidaurre: Brain-computer interfacing in discriminative and stationary subspaces. In Conf proc ieee eng med biol soc, 2012. [Bibtex]

@InProceedings{ 12-samek-Brain-Computer,
author  = {Samek, Wojciech and M{\"u}ller, Klaus-Robert and Kawanabe,
Motoaki and Vidaurre, Carmen},
title = {Brain-Computer Interfacing in Discriminative and
Stationary Subspaces},
booktitle  = {Conf Proc IEEE Eng Med Biol Soc},
year = {2012},
file = {SamEMBS12.pdf:http\://www.user.tu-berlin.de/wojwoj/pdf/SamEMBS12.pdf:PDF},
folder  = {BBCI},
pdf = {http://autonomous-learning.org/wp-content/uploads/SamMüller12.pdf}
}
M. Richter, Y. Sandamirskaya, and G. Schöner: A robotic architecture for action selection and behavioral organization inspired by human cognition. In Ieee/rsj international conference on intelligent robots and systems, iros, 2012. [Bibtex]

@InProceedings{ 12-richter-robotic,
author  = {Richter, M. and Sandamirskaya, Y. and Sch\"oner, G},
title = {A robotic architecture for action selection and behavioral
organization inspired by human cognition},
booktitle  = {IEEE/RSJ International Conference on Intelligent Robots
and Systems, IROS},
year = {2012},
pdf = {http://sandamirskaya.eu/resources/RichterSandamirskayaSchoner2012_IROS.pdf}
}
N. Pinkwart and B. Hammer: Towards learning feedback in intelligent tutoring systems by clustering spaces of student solutions. In Proceedings of the 25th international conference of the florida artificial intelligence research society (flairs), 572, AAAI, 2012. [Bibtex]

@InProceedings{ 12-pinkwart-Towards,
author  = {N. Pinkwart and B. Hammer},
title = {Towards Learning Feedback in Intelligent Tutoring Systems
by Clustering Spaces of Student Solutions},
booktitle  = {Proceedings of the 25th International Conference of the
Florida Artificial Intelligence Research Society (FLAIRS)},
year = {2012},
editor  = {G. M. Youngblood and P. McCarthy},
pages = {572},
address  = {Marco Island, FL, USA},
publisher  = {AAAI},
pdf = {http://hcis.in.tu-clausthal.de/pubs/2012/flairs/towards_learning_feedback_in_intelligent_tutoring_systems_by_clustering_spaces_of_student_solutions.pdf}
}
B. Mokbel, S. Gross, M. Lux, N. Pinkwart, and B. Hammer: How to quantitatively compare data dissimilarities for unsupervised machine learning?. In Annpr, 7477, 1-13, Springer, 2012. [Bibtex]

@InProceedings{ 12-mokbel-How,
author  = {Mokbel, B. and Gross, S. and Lux, M. and Pinkwart, N. and
Hammer, B.},
title = {How to Quantitatively Compare Data Dissimilarities for
Unsupervised Machine Learning?},
booktitle  = {ANNPR},
year = {2012},
editor  = {Mana, Nadia and Schwenker, Friedhelm and Trentin,
Edmondo},
volume  = {7477},
series  = {Lecture Notes in Computer Science},
pages = {1-13},
publisher  = {Springer},
pdf = {http://hcis.in.tu-clausthal.de/pubs/2012/annpr/how_to_quantitatively_compare_data_dissimilarities_for_unsupervised_machine_learning.pdf}
}
G. Martius and J. Herrmann: Variants of guided self-organization for robot control. Theory in biosci., 131, 129-137, Springer Berlin / Heidelberg, 2012. [Bibtex]

@Article{ 12-martius-Variants,
author  = {Martius, Georg and Herrmann, J.~Michael},
affiliation  = {Bernstein Center for Computational Neuroscience and Max
Planck Institute for Dynamics and Self-Organization,
Bunsenstr. 10, 37073 Göttingen, Germany},
title = {Variants of guided self-organization for robot control},
journal  = {Theory in Biosci.},
publisher  = {Springer Berlin / Heidelberg},
issn = {1431-7613},
pages = {129-137},
volume  = 131,
number  = 3,
year = 2012,
pdf = {http://robot.informatik.uni-
leipzig.de/research/publications/2012/martiusherrmann_variantsofgso_12_watermark.pdf},
doi = {10.1007/s12064-011-0141-0}
}
G. Martius: Robustness of guided self-organization against sensorimotor disruptions. Advances in complex systems, accepted 15.Oct 2012, 2012. [Bibtex]

@Article{ 12-martius-Robustness,
author  = {Georg Martius},
journal  = {Advances in Complex Systems},
title = {Robustness of guided self-organization against
sensorimotor disruptions},
year = {2012},
note = {accepted 15.Oct 2012},
pdf = {http://autonomous-learning.org/wp-content/uploads/martius_robustness_of_gso_12_watermark.pdf}
}
M. Lopes, T. Lang, and M. Toussaint: Exploration in model-based reinforcement learning by empirically estimating learning progress. In Neural information processing systems (nips 2012), 2012. [Bibtex]

@InProceedings{ 12-lopes-Exploration,
key = "NIPS",
author  = "Manuel Lopes and Tobias Lang and Marc Toussaint",
title = "Exploration in Model-based Reinforcement Learning by
Empirically Estimating Learning Progress",
booktitle  = "Neural Information Processing Systems (NIPS 2012)",
year = "2012",
url = {http://ipvs.informatik.uni-stuttgart.de/mlr/marc/publications/12-lopes-NIPS.pdf},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/12-lopes-NIPS.pdf}
}
Q. Li, R. Haschke, B. Bolder, and Helge Ritter: Grasp point optimization by online exploration of unknown object surface. 12th ieee-ras intl conf on humanoid robots, 2012. [Bibtex]

@InProceedings{ 12-li-Grasp,
title = {Grasp Point Optimization by Online Exploration of Unknown
Object Surface},
year = {2012},
address  = {Osaka, Japan},
month = {29/11/2012},
author  = {Qiang Li and Robert Haschke and Bram Bolder and Helge
Ritter},
journal  = {12th IEEE-RAS Intl Conf on Humanoid Robots},
pdf = {http://autonomous-learning.org/wp-content/uploads/haschke1.pdf}
}
T. Lang, M. Toussaint, and K. Kersting: Exploration in relational domains for model-based reinforcement learning. Journal of machine learning research, 13, 3691-3734, 2012. [Bibtex]

@Article{ 12-lang-Exploration,
key = "JMLR",
title = "Exploration in Relational Domains for Model-based
Reinforcement Learning",
author  = "Tobias Lang and Marc Toussaint and Kristian Kersting",
journal  = "Journal of Machine Learning Research",
year = "2012",
volume  = "13",
pages = "3691-3734",
url = {https://ipvs.informatik.uni-stuttgart.de/mlr/marc/publications/12-lang-JMLR.pdf},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/12-lang-JMLR.pdf}
}
A. Kumar, S. Zilberstein, and M. Toussaint: Message passing algorithms for map estimation using dc programming. In Int. conf. on artificial intelligence and statistics (aistats 2012), 656-664, 2012. [Bibtex]

@InProceedings{ 12-kumar-Message,
key = "AISTATS",
author  = "Akshat Kumar and Shlomo Zilberstein and Marc Toussaint",
year = "2012",
title = "Message Passing Algorithms for MAP Estimation Using DC
Programming",
booktitle  = "Int.{} Conf.{} on Artificial Intelligence and Statistics
(AISTATS 2012)",
pages = "656-664",
url = {http://ipvs.informatik.uni-stuttgart.de/mlr/marc/publications/12-kumar-AISTATS.pdf},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/12-kumar-AISTATS.pdf}
}
A. Krueger, O. Walter, V. Leutnant, and R. Haeb-Umbach: Bayesian feature enhancement for asr of noisy reverberant real-worlddata. In Proceedings of interspeech, 2012. [Bibtex]

@InProceedings{ 12-krueger-Bayesian,
author  = { A. Krueger and O. Walter and V. Leutnant and R.
Haeb-Umbach},
title = {Bayesian Feature Enhancement for ASR of Noisy Reverberant
Real-WorldData},
booktitle  = {Proceedings of INTERSPEECH},
year = {2012},
pdf = {http://autonomous-learning.org/wp-content/uploads/WalterHaeb-Umbach_Interspeech2012-1.pdf}
}
S. Kazerounian, M. Luciw, M. Richter, and Y. Sandamirskaya: Autonomous reinforcement of behavioral sequences in neural dynamics. In Proceedings of the joint ieee international conference on development and learning & epigenetic robotics (icdl-epirob), 2012. [Bibtex]

@InProceedings{ 12-kazerounian-Autonomous,
author  = {Kazerounian, S. and Luciw, M and Richter, M. and
Sandamirskaya, Y.},
title = {Autonomous Reinforcement of Behavioral Sequences in Neural
Dynamics},
booktitle  = {Proceedings of the Joint IEEE International Conference on
Development and Learning \& Epigenetic Robotics
(ICDL-EPIROB)},
year = {2012},
pdf = {http://arxiv.org/pdf/1210.3569.pdf}
}
U. van Hengel, Y. Sandamirskaya, S. Schneegans, and S. G: A neural-dynamic architecture for flexible spatial language: intrinsic frames, the term “between”, and autonomy. In 21st ieee international symposium on robot and human interactivecommunication (ro-man) 2012, 2012. [Bibtex]

@Conference{ 12-hengel-neural-dynamic,
author  = {van Hengel, U and Sandamirskaya, Y and Schneegans, S and
Sch\"oner G},
title = {A neural-dynamic architecture for flexible spatial
language: intrinsic frames, the term “between”, and
autonomy},
booktitle  = {21st IEEE International Symposium on Robot and Human
InteractiveCommunication (Ro-Man) 2012},
year = {2012},
pdf = {http://sandamirskaya.eu/resources/submissionRoMan2012.pdf}
}
S. Gross, B. Mokbel, B. Hammer, and N. Pinkwart: Feedback provision strategies in intelligent tutoring systems based on clustered solution spaces. In Delfi 2012: die 10. e-learning fachtagung informatik, 27-38, Köllen, 2012. [Bibtex]

@InProceedings{ 12-gross-Feedback,
author  = {Gross, S. and Mokbel, B. and Hammer, B. and Pinkwart, N.},
title = {Feedback Provision Strategies in Intelligent Tutoring
Systems Based on Clustered Solution Spaces},
booktitle  = {DeLFI 2012: Die 10. e-Learning Fachtagung Informatik},
year = {2012},
editor  = {Desel, J\"org and Haake, Joerg M. and Spannagel,
Christian},
pages = {27-38},
address  = {Hagen, Germany},
publisher  = {K\"ollen},
isbn = {978-3885796015},
pdf = {http://hcis.in.tu-clausthal.de/pubs/2012/delfi/feedback_provision_strategies_in_intelligent_tutoring_systems_based_on_clustered_solution_spaces.pdf}
}
S. Gross, X. Zhu, B. Hammer, and N. Pinkwart: Cluster based feedback provision strategies in intelligent tutoring systems. In Proceedings of the 11th international conference on intelligent tutoring systems (its), 7315, 699-700, Springer Verlag, 2012. [Bibtex]

@InProceedings{ 12-gross-Cluster,
author  = {Gross, S. and Zhu, X. and Hammer, B. and Pinkwart, N.},
title = {Cluster Based Feedback Provision Strategies in Intelligent
Tutoring Systems},
booktitle  = {Proceedings of the 11th International Conference on
Intelligent Tutoring Systems (ITS)},
year = {2012},
editor  = {Cerri, Stefano and Clancey, William and Papadourakis,
Giorgos and Panourgia, Kitty},
volume  = {7315},
series  = {Lecture Notes in Computer Science},
pages = {699 - 700},
address  = {Berlin, Germany},
publisher  = {Springer Verlag},
pdf = {http://hcis.in.tu-clausthal.de/pubs/2012/its/cluster_based_feedback_provision_strategies_in_intelligent_tutoring_systems.pdf}
}
J. Fürnkranz, E. Hüllermeier, W. Cheng, and S. Park: Preference-based reinforcement learning: a formal framework and a policy iteration algorithm. Machine learning, 89, 123–156, Special Issue of Selected Papers from ECML PKDD 2011, 2012. [Bibtex]

@Article{ 12-furnkranz-Preference-based,
author  = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke and
Cheng, Weiwei and Park, Sang-Hyeun},
keywords  = {Preference learning, Reinforcement learning},
title = {Preference-based Reinforcement Learning: A Formal
Framework and a Policy Iteration Algorithm},
journal  = {Machine Learning},
volume  = {89},
number  = {1-2},
year = {2012},
pages = {123--156},
note = {Special Issue of Selected Papers from ECML PKDD 2011},
issn = {0885-6125},
doi = {10.1007/s10994-012-5313-8},
abstract  = {This paper makes a first step toward the integration of
two subfields of machine learning, namely preference
learning and reinforcement learning (RL). An important
motivation for a preference-based approach to reinforcement
learning is the observation that in many real-world
domains, numerical feedback signals are not readily
available, or are defined arbitrarily in order to satisfy
the needs of conventional RL algorithms. Instead, we
propose an alternative framework for reinforcement
learning, in which qualitative reward signals can be
directly used by the learner. The framework may be viewed
as a generalization of the conventional RL framework in
which only a partial order between policies is required
instead of the total order induced by their respective
expected long-term reward. Therefore, building on novel
methods for preference learning, our general goal is to
equip the RL agent with qualitative policy models, such as
ranking functions that allow for sorting its available
actions from most to least promising, as well as algorithms
for learning such models from qualitative feedback. As a
proof of concept, we realize a first simple instantiation
of this framework that defines preferences based on
utilities observed for trajectories. To that end, we build
on an existing method for approximate policy iteration
based on roll-outs. While this approach is based on the use
of classification methods for generalization and policy
learning, we make use of a specific type of preference
learning method called label ranking. Advantages of
preference-based approximate policy iteration are
illustrated by means of two case studies.}
}
J. Fürnkranz and E. Hüllermeier: Preference learning. In Encyclopedia of the sciences of learning, 986, Springer-Verlag, 2012. [Bibtex]

@InCollection{ 12-furnkranz-Preference,
author  = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
editor  = {Seel, Norbert M.},
title = {Preference Learning},
booktitle  = {Encyclopedia of the Sciences of Learning},
year = {2012},
pages = {986},
publisher  = {Springer-Verlag}
}
B. Duran and Y. Sandamirskaya: Neural dynamics of hierarchically organized sequences: a robotic implementation. In The ieee-ras international conference on humanoid robots (humanoids), 2012. [Bibtex]

@Conference{ 12-duran-Neural,
author  = {Duran, B and Sandamirskaya, Y},
title = {Neural Dynamics of Hierarchically Organized Sequences: a
Robotic Implementation},
booktitle  = {The IEEE-RAS International Conference on Humanoid Robots
(Humanoids)},
year = {2012}
}
B. Duran, Y. Sandamirskaya, and G. Schöner: A dynamic field architecture for the generation of hierarchically organized sequences. In Artificial neural networks and machine learning – icann 2012, 7552, 25-32, Springer Berlin Heidelberg, 2012. [Bibtex]

@InCollection{ 12-duran-Dynamic,
author  = {Duran, Boris and Sandamirskaya, Yulia and Sch{\"o}ner,
Gregor},
title = {A Dynamic Field Architecture for the Generation of
Hierarchically Organized Sequences},
booktitle  = {Artificial Neural Networks and Machine Learning – ICANN
2012},
publisher  = {Springer Berlin Heidelberg},
year = {2012},
editor  = {Villa, AlessandroE.P. and Duch, Włodzisław and Érdi,
Péter and Masulli, Francesco and Palm, Günther},
volume  = {7552},
series  = {Lecture Notes in Computer Science},
pages = {25-32},
pdf = {http://dx.doi.org/10.1007/978-3-642-33269-2_4}
}
W. Böhmer, S. Grünewälder, H. Nickisch, and K. Obermayer: Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis. Machine learning, 89, 67–86, 2012. [Bibtex]

@Article{ 12-böhmer-Generating,
title = {Generating feature spaces for linear algorithms with
regularized sparse kernel slow feature analysis},
author  = {Böhmer, W. and Grünewälder, S. and Nickisch, H. and
Obermayer, K.},
pages = {67--86},
year = {2012},
journal  = {Machine Learning},
volume  = {89},
number  = {1},
abstract  = {Without non-linear basis functions many problems can not
be solved by linear algorithms. This article proposes a
method to automatically construct such basis functions with
slow feature analysis (SFA). Non-linear optimization of
this unsupervised learning method generates an orthogonal
basis on the unknown latent space for a given time series.
In contrast to methods like PCA, SFA is thus well suited
for techniques that make direct use of the latent space.
Real-world time series can be complex, and current SFA
algorithms are either not powerful enough or tend to
over-fit. We make use of the kernel trick in combination
with sparsification to develop a kernelized SFA algorithm
which provides a powerful function class for large data
sets. Sparsity is achieved by a novel matching pursuit
approach that can be applied to other tasks as well. For
small data sets, however, the kernel SFA approach leads to
over-fitting and numerical instabilities. To enforce a
stable solution, we introduce regularization to the SFA
objective. We hypothesize that our algorithm generates a
feature space that resembles a Fourier basis in the unknown
space of latent variables underlying a given real-world
time series. We evaluate this hypothesis at the example of
a vowel classification task in comparison to sparse kernel
PCA. Our results show excellent classification accuracy and
demonstrate the superiority of kernel SFA over kernel PCA
in encoding latent variables.},
pdf = {http://www.ni.tu-berlin.de/fileadmin/fg215/articles/Generating_feature_spaces.pdf}
}
G. Büscher, R. Kõiva, Carsten Schürmann, R. Haschke, and H. J. Ritter: Tactile dataglove with fabric-based sensors. In Ieee-ras international conference on humanoid robots (humanoids 2012), 2012. [Bibtex]

@Conference{ 12-buscher-Tactile,
author  = {Gereon B{\"u}scher and Risto K{\~o}iva and Carsten
Sch{\"u}rmann and Robert Haschke and Helge J. Ritter},
title = {Tactile dataglove with fabric-based sensors},
booktitle  = {IEEE-RAS International Conference on Humanoid Robots
(Humanoids 2012)},
year = {2012},
address  = {Osaka, Japan},
month = {29/11/2012}
}
G. Büscher, R. Kõiva, Carsten Schürmann, R. Haschke, and H. Ritter: Flexible and stretchable fabric-based tactile sensor. In Ieee/rsj international conference on intelligent robots and systems (iros 2012) workshop on advances in tactile sensing and touch based human-robot interaction, 2012. [Bibtex]

@Conference{ 12-buscher-Flexible,
author  = {Gereon B{\"u}scher and Risto K{\~o}iva and Carsten
Sch{\"u}rmann and Robert Haschke and Helge Ritter},
title = {Flexible and stretchable fabric-based tactile sensor},
booktitle  = {IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS 2012) Workshop on Advances in Tactile
Sensing and Touch based Human-Robot Interaction},
year = {2012},
address  = {Vilamoura, Algarve, Portugal},
month = {11/10/2012},
pdf = {http://autonomous-learning.org/wp-content/uploads/haschke3.pdf}
}
M. Botvinick and M. Toussaint: Planning as probabilistic inference. Trends in cognitive sciences, 16, 485-488, 2012. [Bibtex]

@Article{ 12-botvinick-Planning,
key = "TICS",
author  = "Matthew Botvinick and Marc Toussaint",
title = "Planning as probabilistic inference",
journal  = "Trends in Cognitive Sciences",
volume  = "16",
pages = "485-488",
year = "2012",
url = {http://ipvs.informatik.uni-stuttgart.de/mlr/marc/publications/12-botvinick-TICS.pdf},
pdf = {https://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/12-botvinick-TICS.pdf}
}
W. Böhmer, S. Grünewälder, Hannes Nickisch, and K. Obermayer: Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis. Machine learning, 89, 67–86, Springer US, 2012. [Bibtex]

@Article{ 12-bohmer-Generating,
author  = {Wendelin B\"ohmer and Steffen Gr\"unew\"alder and Hannes
Nickisch and Klaus Obermayer},
title = {Generating Feature Spaces for Linear Algorithms with
Regularized Sparse Kernel Slow Feature Analysis},
journal  = {Machine Learning},
publisher  = {Springer US},
pages = {67--86},
volume  = {89},
number  = {1-2},
year = {2012},
url = {http://www.ni.tu-berlin.de/fileadmin/fg215/articles/Generating_feature_spaces.pdf}
}
J. J. Alcaraz-Jiménez, M. Missura, and H. M. S. Behnke: Lateral disturbance rejection for the nao robot. In In proceedings of 16th robocup international symposium, Springer, Best Paper Award, 2012. [Bibtex]

@InProceedings{ 12-alcaraz-jiménez-Lateral,
author  = {Juan José Alcaraz-Jiménez and Marcell Missura and
Humberto Martínez-Barberáand Sven Behnke},
title = {Lateral Disturbance Rejection for the Nao Robot},
booktitle  = {In Proceedings of 16th RoboCup International Symposium},
year = {2012},
publisher  = {Springer},
note = {Best Paper Award},
pdf = {http://autonomous-learning.org/wp-content/uploads/Lateral_Disturbance_Rejection.pdf}
}

2011

W. Samek, M. Kawanabe, and C. Vidaurre: Group-wise stationary subspace analysis – a novel method for studying non-stationarities. In Proc. 5th int. bci conf. graz, 16-20, Verlag der Technischen Universität Graz, 2011. [Bibtex]

@InCollection{ 11-samek-Group-wise,
author  = {Samek, Wojciech and Kawanabe, Motoaki and Vidaurre,
Carmen},
title = {Group-wise Stationary Subspace Analysis - A novel method
for studying non-stationarities},
booktitle  = {Proc. 5th Int. BCI Conf. Graz},
publisher  = {Verlag der Technischen Universit{\"a}t Graz},
year = {2011},
editor  = {M{\"u}ller-Putz, G. R. and Scherer, R. and Billinger, M.
and Kreilinger, A. and Kaiser, V. and Neuper, C.},
pages = {16-20},
address  = {Graz},
folder  = {BBCI},
isbn = {978-3-85125-140-1},
pdf = {http://autonomous-learning.org/wp-content/uploads/SamBCI11.pdf}
}

2010

C. Sannelli, C. Vidaurre, K. Müller, and B. Blankertz: Common spatial pattern patches – an optimized filter ensemble for adaptive brain-computer interfaces. In Conf proc ieee eng med biol soc, 2010, 4351–4354, 2010. [Bibtex]

@InProceedings{ 10-sannelli-Common,
author  = {Sannelli, Claudia and Vidaurre, Carmen and M\"uller,
Klaus-Robert and Blankertz, Benjamin},
title = {Common Spatial Pattern Patches - an Optimized Filter
Ensemble for Adaptive Brain-Computer Interfaces},
booktitle  = {Conf Proc IEEE Eng Med Biol Soc},
year = {2010},
volume  = {2010},
pages = {4351--4354},
file = {SanVidMueBla10.pdf:http\://doc.ml.tu-berlin.de/bbci/publications/SanVidMueBla10.pdf:PDF},
folder  = {BBCI},
grants  = {Vital-BCI,BFNT,brain@work,TOBI,Pascal2},
pdf = {http://dx.doi.org/10.1109/IEMBS.2010.5626227}
}