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2017


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Flexible Spatio-Temporal Networks for Video Prediction

Lu, C., Hirsch, M., Schölkopf, B.

Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pages: 2137-2145, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (conference)

ei

link (url) DOI [BibTex]

2017


link (url) DOI [BibTex]


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Discovering Causal Signals in Images

Lopez-Paz, D., Nishihara, R., Chintala, S., Schölkopf, B., Bottou, L.

Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pages: 58-66, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (conference)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Dynamic Time-of-Flight

Schober, M., Adam, A., Yair, O., Mazor, S., Nowozin, S.

Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pages: 170-179, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (conference)

ei pn

DOI [BibTex]

DOI [BibTex]


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Strategic exploration in human adaptive control

Schulz, E., Klenske, E., Bramley, N., Speekenbrink, M.

Proceedings of the 39th Annual Conference of the Cognitive Science Society (CogSci), (Editors: Glenn Gunzelmann, Andrew Howes, Thora Tenbrink and Eddy J. Davelaar), cognitivesciencesociety.org, July 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Learning Movement Primitive Libraries through Probabilistic Segmentation

Lioutikov, R., Neumann, G., Maeda, G., Peters, J.

International Journal of Robotics Research, 36(8):879-894, July 2017 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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State-Regularized Policy Search for Linearized Dynamical Systems

Abdulsamad, H., Arenz, O., Peters, J., Neumann, G.

Proceedings of the Twenty-Seventh International Conference on Automated Planning and Scheduling, (ICAPS), pages: 419-424, (Editors: Laura Barbulescu, Jeremy Frank, Mausam and Stephen F. Smith), AAAI Press, June 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


Virtual vs. {R}eal: Trading Off Simulations and Physical Experiments in Reinforcement Learning with {B}ayesian Optimization
Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization

Marco, A., Berkenkamp, F., Hennig, P., Schoellig, A. P., Krause, A., Schaal, S., Trimpe, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 1557-1563, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (inproceedings)

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PDF arXiv ICRA 2017 Spotlight presentation Virtual vs. Real - Video explanation DOI Project Page [BibTex]

PDF arXiv ICRA 2017 Spotlight presentation Virtual vs. Real - Video explanation DOI Project Page [BibTex]


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Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates

Gu*, S., Holly*, E., Lillicrap, T., Levine, S.

Proceedings 2017 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017, *equal contribution (conference)

ei

Arxiv Project Page [BibTex]

Arxiv Project Page [BibTex]


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Context-Driven Movement Primitive Adaptation

Wilbers, D., Lioutikov, R., Peters, J.

IEEE International Conference on Robotics and Automation (ICRA), pages: 3469-3475, IEEE, May 2017 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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A Learning-based Shared Control Architecture for Interactive Task Execution

Farraj, F. B., Osa, T., Pedemonte, N., Peters, J., Neumann, G., Giordano, P.

IEEE International Conference on Robotics and Automation (ICRA), pages: 329-335, IEEE, May 2017 (conference)

ei

DOI Project Page Project Page [BibTex]

DOI Project Page Project Page [BibTex]


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Frequency Peak Features for Low-Channel Classification in Motor Imagery Paradigms

Jayaram, V., Schölkopf, B., Grosse-Wentrup, M.

Proceedings of the 8th International IEEE/EMBS Conference on Neural Engineering (NER), pages: 321-324, May 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Empowered skills

Gabriel, A., Akrour, R., Peters, J., Neumann, G.

IEEE International Conference on Robotics and Automation (ICRA), pages: 6435-6441, IEEE, May 2017 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Layered direct policy search for learning hierarchical skills

End, F., Akrour, R., Peters, J., Neumann, G.

IEEE International Conference on Robotics and Automation (ICRA), pages: 6442-6448, IEEE, May 2017 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic

Gu, S., Lillicrap, T., Ghahramani, Z., Turner, R. E., Levine, S.

Proceedings International Conference on Learning Representations (ICLR), OpenReviews.net, International Conference on Learning Representations, April 2017 (conference)

ei

PDF link (url) Project Page [BibTex]

PDF link (url) Project Page [BibTex]


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Categorical Reparametrization with Gumbel-Softmax

Jang, E., Gu, S., Poole, B.

Proceedings International Conference on Learning Representations 2017, OpenReviews.net, International Conference on Learning Representations, April 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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DeepCoder: Learning to Write Programs

Balog, M., Gaunt, A. L., Brockschmidt, M., Nowozin, S., Tarlow, D.

Proceedings International Conference on Learning Representations 2017, OpenReviews.net, International Conference on Learning Representations, April 2017 (conference)

ei

Arxiv link (url) Project Page [BibTex]

Arxiv link (url) Project Page [BibTex]


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Estimating B0 inhomogeneities with projection FID navigator readouts

Loktyushin, A., Ehses, P., Schölkopf, B., Scheffler, K.

25th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), April 2017 (poster)

ei

link (url) [BibTex]

link (url) [BibTex]


Distilling Information Reliability and Source Trustworthiness from Digital Traces
Distilling Information Reliability and Source Trustworthiness from Digital Traces

Tabibian, B., Valera, I., Farajtabar, M., Song, L., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the 26th International Conference on World Wide Web (WWW), pages: 847-855, (Editors: Barrett, R., Cummings, R., Agichtein, E. and Gabrilovich, E. ), ACM, April 2017 (conference)

ei

Project DOI Project Page Project Page [BibTex]

Project DOI Project Page Project Page [BibTex]


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Image Quality Improvement by Applying Retrospective Motion Correction on Quantitative Susceptibility Mapping and R2*

Feng, X., Loktyushin, A., Deistung, A., Reichenbach, J.

25th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), April 2017 (poster)

ei

link (url) [BibTex]

link (url) [BibTex]


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Guiding Trajectory Optimization by Demonstrated Distributions

Osa, T., Ghalamzan E., A. M., Stolkin, R., Lioutikov, R., Peters, J., Neumann, G.

IEEE Robotics and Automation Letters, 2(2):819-826, April 2017 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Local Group Invariant Representations via Orbit Embeddings

Raj, A., Kumar, A., Mroueh, Y., Fletcher, T., Schölkopf, B.

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 54, pages: 1225-1235, Proceedings of Machine Learning Research, (Editors: Aarti Singh and Jerry Zhu), April 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Whole-body multi-contact motion in humans and humanoids: Advances of the CoDyCo European project

Padois, V., Ivaldi, S., Babic, J., Mistry, M., Peters, J., Nori, F.

Robotics and Autonomous Systems, 90, pages: 97-117, April 2017, Special Issue on New Research Frontiers for Intelligent Autonomous Systems (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets

Klein, A., Falkner, S., Bartels, S., Hennig, P., Hutter, F.

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017), 54, pages: 528-536, Proceedings of Machine Learning Research, (Editors: Sign, Aarti and Zhu, Jerry), PMLR, April 2017 (conference)

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pdf link (url) Project Page [BibTex]

pdf link (url) Project Page [BibTex]


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Pre-Movement Contralateral EEG Low Beta Power Is Modulated with Motor Adaptation Learning

Ozdenizci, O., Yalcin, M., Erdogan, A., Patoglu, V., Grosse-Wentrup, M., Cetin, M.

International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages: 934-938, March 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Probabilistic Movement Primitives for Coordination of Multiple Human-Robot Collaborative Tasks

Maeda, G., Neumann, G., Ewerton, M., Lioutikov, R., Kroemer, O., Peters, J.

Autonomous Robots, 41(3):593-612, March 2017 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Automatic detection of motion artifacts in MR images using CNNs

Meding, K., Loktyushin, A., Hirsch, M.

42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages: 811-815, March 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Bioinspired tactile sensor for surface roughness discrimination

Yi, Z., Zhang, Y., Peters, J.

Sensors and Actuators A: Physical, 255, pages: 46-53, March 2017 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Catching heuristics are optimal control policies

Belousov, B., Neumann, G., Rothkopf, C., Peters, J.

Proceedings of the Thirteenth Karniel Computational Motor Control Workshop, March 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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DiSMEC – Distributed Sparse Machines for Extreme Multi-label Classification

Babbar, R., Schölkopf, B.

Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM), pages: 721-729, Febuary 2017 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Policy Search with High-Dimensional Context Variables

Tangkaratt, V., van Hoof, H., Parisi, S., Neumann, G., Peters, J., Sugiyama, M.

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI), pages: 2632-2638, (Editors: Satinder P. Singh and Shaul Markovitch), AAAI Press, Febuary 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Model-based Contextual Policy Search for Data-Efficient Generalization of Robot Skills

Kupcsik, A., Deisenroth, M., Peters, J., Ai Poh, L., Vadakkepat, V., Neumann, G.

Artificial Intelligence, 247, pages: 415-439, 2017, Special Issue on AI and Robotics (article)

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link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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Anticipatory Action Selection for Human-Robot Table Tennis

Wang, Z., Boularias, A., Mülling, K., Schölkopf, B., Peters, J.

Artificial Intelligence, 247, pages: 399-414, 2017, Special Issue on AI and Robotics (article)

Abstract
Abstract Anticipation can enhance the capability of a robot in its interaction with humans, where the robot predicts the humans' intention for selecting its own action. We present a novel framework of anticipatory action selection for human-robot interaction, which is capable to handle nonlinear and stochastic human behaviors such as table tennis strokes and allows the robot to choose the optimal action based on prediction of the human partner's intention with uncertainty. The presented framework is generic and can be used in many human-robot interaction scenarios, for example, in navigation and human-robot co-manipulation. In this article, we conduct a case study on human-robot table tennis. Due to the limited amount of time for executing hitting movements, a robot usually needs to initiate its hitting movement before the opponent hits the ball, which requires the robot to be anticipatory based on visual observation of the opponent's movement. Previous work on Intention-Driven Dynamics Models (IDDM) allowed the robot to predict the intended target of the opponent. In this article, we address the problem of action selection and optimal timing for initiating a chosen action by formulating the anticipatory action selection as a Partially Observable Markov Decision Process (POMDP), where the transition and observation are modeled by the \{IDDM\} framework. We present two approaches to anticipatory action selection based on the \{POMDP\} formulation, i.e., a model-free policy learning method based on Least-Squares Policy Iteration (LSPI) that employs the \{IDDM\} for belief updates, and a model-based Monte-Carlo Planning (MCP) method, which benefits from the transition and observation model by the IDDM. Experimental results using real data in a simulated environment show the importance of anticipatory action selection, and that \{POMDPs\} are suitable to formulate the anticipatory action selection problem by taking into account the uncertainties in prediction. We also show that existing algorithms for POMDPs, such as \{LSPI\} and MCP, can be applied to substantially improve the robot's performance in its interaction with humans.

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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easyGWAS: A Cloud-based Platform for Comparing the Results of Genome-wide Association Studies

Grimm, D., Roqueiro, D., Salome, P., Kleeberger, S., Greshake, B., Zhu, W., Liu, C., Lippert, C., Stegle, O., Schölkopf, B., Weigel, D., Borgwardt, K.

The Plant Cell, 29(1):5-19, 2017 (article)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation

Katiyar, P., Divine, M. R., Kohlhofer, U., Quintanilla-Martinez, L., Schölkopf, B., Pichler, B. J., Disselhorst, J. A.

Molecular Imaging and Biology, 19(3):391-397, 2017 (article)

ei

DOI [BibTex]

DOI [BibTex]


Early Stopping Without a Validation Set
Early Stopping Without a Validation Set

Mahsereci, M., Balles, L., Lassner, C., Hennig, P.

arXiv preprint arXiv:1703.09580, 2017 (article)

Abstract
Early stopping is a widely used technique to prevent poor generalization performance when training an over-expressive model by means of gradient-based optimization. To find a good point to halt the optimizer, a common practice is to split the dataset into a training and a smaller validation set to obtain an ongoing estimate of the generalization performance. In this paper we propose a novel early stopping criterion which is based on fast-to-compute, local statistics of the computed gradients and entirely removes the need for a held-out validation set. Our experiments show that this is a viable approach in the setting of least-squares and logistic regression as well as neural networks.

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link (url) Project Page Project Page [BibTex]


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Elements of Causal Inference - Foundations and Learning Algorithms

Peters, J., Janzing, D., Schölkopf, B.

Adaptive Computation and Machine Learning Series, The MIT Press, Cambridge, MA, USA, 2017 (book)

ei

PDF [BibTex]

PDF [BibTex]


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Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning

Roos, F. D., Hennig, P.

arXiv preprint arXiv:1706.00241, 2017 (article)

Abstract
Solving symmetric positive definite linear problems is a fundamental computational task in machine learning. The exact solution, famously, is cubicly expensive in the size of the matrix. To alleviate this problem, several linear-time approximations, such as spectral and inducing-point methods, have been suggested and are now in wide use. These are low-rank approximations that choose the low-rank space a priori and do not refine it over time. While this allows linear cost in the data-set size, it also causes a finite, uncorrected approximation error. Authors from numerical linear algebra have explored ways to iteratively refine such low-rank approximations, at a cost of a small number of matrix-vector multiplications. This idea is particularly interesting in the many situations in machine learning where one has to solve a sequence of related symmetric positive definite linear problems. From the machine learning perspective, such deflation methods can be interpreted as transfer learning of a low-rank approximation across a time-series of numerical tasks. We study the use of such methods for our field. Our empirical results show that, on regression and classification problems of intermediate size, this approach can interpolate between low computational cost and numerical precision.

pn

link (url) Project Page [BibTex]


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Minimax Estimation of Kernel Mean Embeddings

Tolstikhin, I., Sriperumbudur, B., Muandet, K.

Journal of Machine Learning Research, 18(86):1-47, 2017 (article)

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link (url) Project Page [BibTex]


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Kernel Mean Embedding of Distributions: A Review and Beyond

Muandet, K., Fukumizu, K., Sriperumbudur, B., Schölkopf, B.

Foundations and Trends in Machine Learning, 10(1-2):1-141, 2017 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings

Kanagawa, M., Sriperumbudur, B. K., Fukumizu, K.

Arxiv e-prints, arXiv:1709.00147v1 [math.NA], 2017 (article)

Abstract
This paper presents convergence analysis of kernel-based quadrature rules in misspecified settings, focusing on deterministic quadrature in Sobolev spaces. In particular, we deal with misspecified settings where a test integrand is less smooth than a Sobolev RKHS based on which a quadrature rule is constructed. We provide convergence guarantees based on two different assumptions on a quadrature rule: one on quadrature weights, and the other on design points. More precisely, we show that convergence rates can be derived (i) if the sum of absolute weights remains constant (or does not increase quickly), or (ii) if the minimum distance between distance design points does not decrease very quickly. As a consequence of the latter result, we derive a rate of convergence for Bayesian quadrature in misspecified settings. We reveal a condition on design points to make Bayesian quadrature robust to misspecification, and show that, under this condition, it may adaptively achieve the optimal rate of convergence in the Sobolev space of a lesser order (i.e., of the unknown smoothness of a test integrand), under a slightly stronger regularity condition on the integrand.

pn

arXiv [BibTex]

arXiv [BibTex]


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Prediction of intention during interaction with iCub with Probabilistic Movement Primitives

Dermy, O., Paraschos, A., Ewerton, M., Charpillet, F., Peters, J., Ivaldi, S.

Frontiers in Robotics and AI, 4, pages: 45, 2017 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Iterative Feedback-basierte Korrekturstrategien beim Bewegungslernen von Mensch-Roboter-Dyaden

Ewerton, M., Kollegger, G., Maeda, G., Wiemeyer, J., Peters, J.

In DVS Sportmotorik 2017, 2017 (inproceedings)

ei

link (url) [BibTex]

link (url) [BibTex]


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BIMROB - Bidirectional Interaction between human and robot for the learning of movements - Robot trains human - Human trains robot

Kollegger, G., Wiemeyer, J., Ewerton, M., Peters, J.

In Inovation & Technologie im Sport - 23. Sportwissenschaftlicher Hochschultag der deutschen Vereinigung für Sportwissenschaft, pages: 179, (Editors: A. Schwirtz, F. Mess, Y. Demetriou & V. Senner ), Czwalina-Feldhaus, 2017 (inproceedings)

ei

[BibTex]

[BibTex]


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Manifold-based multi-objective policy search with sample reuse

Parisi, S., Pirotta, M., Peters, J.

Neurocomputing, 263, pages: 3-14, (Editors: Madalina Drugan, Marco Wiering, Peter Vamplew, and Madhu Chetty), 2017, Special Issue on Multi-Objective Reinforcement Learning (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Robot Learning

Peters, J., Lee, D., Kober, J., Nguyen-Tuong, D., Bagnell, J., Schaal, S.

In Springer Handbook of Robotics, pages: 357-394, 15, 2nd, (Editors: Siciliano, Bruno and Khatib, Oussama), Springer International Publishing, 2017 (inbook)

am ei

Project Page [BibTex]

Project Page [BibTex]


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BIMROB – Bidirektionale Interaktion von Mensch und Roboter beim Bewegungslernen

Wiemeyer, J., Peters, J., Kollegger, G., Ewerton, M.

DVS Sportmotorik 2017, 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Spectral Clustering predicts tumor tissue heterogeneity using dynamic 18F-FDG PET: a complement to the standard compartmental modeling approach

Katiyar, P., Divine, M. R., Kohlhofer, U., Quintanilla-Martinez, L., Schölkopf, B., Pichler, B. J., Disselhorst, J. A.

Journal of Nuclear Medicine, 58(4):651-657, 2017 (article)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Electroencephalographic identifiers of motor adaptation learning

Ozdenizci, O., Yalcin, M., Erdogan, A., Patoglu, V., Grosse-Wentrup, M., Cetin, M.

Journal of Neural Engineering, 14(4):046027, 2017 (article)

ei

link (url) [BibTex]

link (url) [BibTex]