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2019


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Enhancing Human Learning via Spaced Repetition Optimization

Tabibian, B., Upadhyay, U., De, A., Zarezade, A., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the National Academy of Sciences, 2019, PNAS published ahead of print January 22, 2019 (article)

DOI Project Page Project Page [BibTex]

2019

DOI Project Page Project Page [BibTex]


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Spatial Filtering based on Riemannian Manifold for Brain-Computer Interfacing

Xu, J.

Technical University of Munich, Germany, 2019 (mastersthesis)

[BibTex]

[BibTex]


Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots
Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots

Büchler, D., Calandra, R., Peters, J.

2019 (article) Submitted

Abstract
High-speed and high-acceleration movements are inherently hard to control. Applying learning to the control of such motions on anthropomorphic robot arms can improve the accuracy of the control but might damage the system. The inherent exploration of learning approaches can lead to instabilities and the robot reaching joint limits at high speeds. Having hardware that enables safe exploration of high-speed and high-acceleration movements is therefore desirable. To address this issue, we propose to use robots actuated by Pneumatic Artificial Muscles (PAMs). In this paper, we present a four degrees of freedom (DoFs) robot arm that reaches high joint angle accelerations of up to 28000 °/s^2 while avoiding dangerous joint limits thanks to the antagonistic actuation and limits on the air pressure ranges. With this robot arm, we are able to tune control parameters using Bayesian optimization directly on the hardware without additional safety considerations. The achieved tracking performance on a fast trajectory exceeds previous results on comparable PAM-driven robots. We also show that our system can be controlled well on slow trajectories with PID controllers due to careful construction considerations such as minimal bending of cables, lightweight kinematics and minimal contact between PAMs and PAMs with the links. Finally, we propose a novel technique to control the the co-contraction of antagonistic muscle pairs. Experimental results illustrate that choosing the optimal co-contraction level is vital to reach better tracking performance. Through the use of PAM-driven robots and learning, we do a small step towards the future development of robots capable of more human-like motions.

Arxiv Video [BibTex]


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AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs

Abbati*, G., Wenk*, P., Osborne, M. A., Krause, A., Schölkopf, B., Bauer, S.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 1-10, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, 2019, *equal contribution (conference)

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Perception of temporal dependencies in autoregressive motion

Meding, K., Schölkopf, B., Wichmann, F. A.

European Conference on Visual Perception (ECVP), 2019 (poster)

[BibTex]


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Inferring causation from time series with perspectives in Earth system sciences

Runge, J., Bathiany, S., Bollt, E., Camps-Valls, G., Coumou, D., Deyle, E., Glymour, C., Kretschmer, M., Mahecha, M., van Nes, E., Peters, J., Quax, R., Reichstein, M., Scheffer, M. S. B., Spirtes, P., Sugihara, G., Sun, J., Zhang, K., Zscheischler, J.

Nature Communications, 2019 (article) In revision

[BibTex]

[BibTex]


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Quantum mean embedding of probability distributions

Kübler, J. M., Muandet, K., Schölkopf, B.

Physical Review Research, 1(3):033159, American Physical Society, 2019 (article)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Quantification of tumor heterogeneity using PET/MRI and machine learning

Katiyar, P.

Eberhard Karls Universität Tübingen, Germany, 2019 (phdthesis)

[BibTex]

[BibTex]


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MYND: A Platform for Large-scale Neuroscientific Studies

Hohmann, M. R., Hackl, M., Wirth, B., Zaman, T., Enficiaud, R., Grosse-Wentrup, M., Schölkopf, B.

Proceedings of the 2019 Conference on Human Factors in Computing Systems (CHI), 2019 (conference) Accepted

[BibTex]

[BibTex]


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A Kernel Stein Test for Comparing Latent Variable Models

Kanagawa, H., Jitkrittum, W., Mackey, L., Fukumizu, K., Gretton, A.

2019 (conference) Submitted

arXiv [BibTex]

arXiv [BibTex]


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Phenomenal Causality and Sensory Realism

Bruijns, S. A., Meding, K., Schölkopf, B., Wichmann, F. A.

European Conference on Visual Perception (ECVP), 2019 (poster)

[BibTex]

[BibTex]


From Variational to Deterministic Autoencoders
From Variational to Deterministic Autoencoders

Ghosh*, P., Sajjadi*, M. S. M., Vergari, A., Black, M. J., Schölkopf, B.

2019, *equal contribution (conference) Submitted

Abstract
Variational Autoencoders (VAEs) provide a theoretically-backed framework for deep generative models. However, they often produce “blurry” images, which is linked to their training objective. Sampling in the most popular implementation, the Gaussian VAE, can be interpreted as simply injecting noise to the input of a deterministic decoder. In practice, this simply enforces a smooth latent space structure. We challenge the adoption of the full VAE framework on this specific point in favor of a simpler, deterministic one. Specifically, we investigate how substituting stochasticity with other explicit and implicit regularization schemes can lead to a meaningful latent space without having to force it to conform to an arbitrarily chosen prior. To retrieve a generative mechanism for sampling new data points, we propose to employ an efficient ex-post density estimation step that can be readily adopted both for the proposed deterministic autoencoders as well as to improve sample quality of existing VAEs. We show in a rigorous empirical study that regularized deterministic autoencoding achieves state-of-the-art sample quality on the common MNIST, CIFAR-10 and CelebA datasets.

arXiv [BibTex]


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Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces

Klus, S., Schuster, I., Muandet, K.

Journal of Nonlinear Science, 2019, First Online: 21 August 2019 (article)

DOI [BibTex]

DOI [BibTex]

2016


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Consistent Kernel Mean Estimation for Functions of Random Variables

Simon-Gabriel*, C. J., Ścibior*, A., Tolstikhin, I., Schölkopf, B.

Advances in Neural Information Processing Systems 29, pages: 1732-1740, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems, December 2016, *joint first authors (conference)

link (url) Project Page Project Page Project Page [BibTex]

2016

link (url) Project Page Project Page Project Page [BibTex]


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Understanding Probabilistic Sparse Gaussian Process Approximations

Bauer, M., van der Wilk, M., Rasmussen, C. E.

Advances in Neural Information Processing Systems 29, pages: 1533-1541, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems, December 2016 (conference)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels

Tolstikhin, I., Sriperumbudur, B. K., Schölkopf, B.

Advances in Neural Information Processing Systems 29, pages: 1930-1938, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems, December 2016 (conference)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Local-utopia Policy Selection for Multi-objective Reinforcement Learning

Parisi, S., Blank, A., Viernickel, T., Peters, J.

In IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), pages: 1-7, IEEE, December 2016 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Lifelong Learning with Weighted Majority Votes

Pentina, A., Urner, R.

Advances in Neural Information Processing Systems 29, pages: 3612-3620, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems, December 2016 (conference)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Active Nearest-Neighbor Learning in Metric Spaces

Kontorovich, A., Sabato, S., Urner, R.

Advances in Neural Information Processing Systems 29, pages: 856-864, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems, December 2016 (conference)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Catching heuristics are optimal control policies

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

Advances in Neural Information Processing Systems 29, pages: 1426-1434, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems, December 2016 (conference)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Incremental Imitation Learning of Context-Dependent Motor Skills

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

IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pages: 351-358, IEEE, November 2016 (conference)

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Using Probabilistic Movement Primitives for Striking Movements

Gomez-Gonzalez, S., Neumann, G., Schölkopf, B., Peters, J.

16th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pages: 502-508, November 2016 (conference)

link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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Demonstration Based Trajectory Optimization for Generalizable Robot Motions

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

IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pages: 351-358, IEEE, November 2016 (conference)

DOI Project Page [BibTex]

DOI Project Page [BibTex]


Jointly Learning Trajectory Generation and Hitting Point Prediction in Robot Table Tennis
Jointly Learning Trajectory Generation and Hitting Point Prediction in Robot Table Tennis

Huang, Y., Büchler, D., Koc, O., Schölkopf, B., Peters, J.

16th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pages: 650-655, November 2016 (conference)

final link (url) DOI Project Page [BibTex]

final link (url) DOI Project Page [BibTex]


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Deep Spiking Networks for Model-based Planning in Humanoids

Tanneberg, D., Paraschos, A., Peters, J., Rueckert, E.

IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pages: 656-661, IEEE, November 2016 (conference)

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Anticipative Interaction Primitives for Human-Robot Collaboration

Maeda, G., Maloo, A., Ewerton, M., Lioutikov, R., Peters, J.

AAAI Fall Symposium Series. Shared Autonomy in Research and Practice, pages: 325-330, November 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Unifying distillation and privileged information

Lopez-Paz, D., Schölkopf, B., Bottou, L., Vapnik, V.

International Conference on Learning Representations (ICLR), November 2016 (conference)

Arxiv Project Page [BibTex]

Arxiv Project Page [BibTex]


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Learning High-Order Filters for Efficient Blind Deconvolution of Document Photographs

Xiao, L., Wang, J., Heidrich, W., Hirsch, M.

Computer Vision - ECCV 2016, Lecture Notes in Computer Science, LNCS 9907, Part III, pages: 734-749, (Editors: Bastian Leibe, Jiri Matas, Nicu Sebe and Max Welling), Springer, October 2016 (conference)

DOI [BibTex]

DOI [BibTex]


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Adaptive Training Strategies for BCIs

Sharma, D., Tanneberg, D., Grosse-Wentrup, M., Peters, J., Rueckert, E.

Cybathlon Symposium, October 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Experiments with Hierarchical Reinforcement Learning of Multiple Grasping Policies

Osa, T., Peters, J., Neumann, G.

International Symposium on Experimental Robotics (ISER), 1, pages: 160-172, Springer Proceedings in Advanced Robotics, (Editors: Dana Kulic, Yoshihiko Nakamura, Oussama Khatib and Gentiane Venture), Springer, October 2016 (conference)

DOI [BibTex]

DOI [BibTex]


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Stable Reinforcement Learning with Autoencoders for Tactile and Visual Data

van Hoof, H., Chen, N., Karl, M., van der Smagt, P., Peters, J.

Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), pages: 3928-3934, IEEE, October 2016 (conference)

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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A New Trajectory Generation Framework in Robotic Table Tennis

Koc, O., Maeda, G., Peters, J.

Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), pages: 3750-3756, October 2016 (conference)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Probabilistic Decomposition of Sequential Force Interaction Tasks into Movement Primitives

Manschitz, S., Gienger, M., Kober, J., Peters, J.

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 3920-3927, IEEE, October 2016 (conference)

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Multi-task logistic regression in brain-computer interfaces

Fiebig, K., Jayaram, V., Peters, J., Grosse-Wentrup, M.

6th Workshop on Brain-Machine Interface Systems at IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016), pages: 002307-002312, IEEE, October 2016 (conference)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Active Tactile Object Exploration with Gaussian Processes

Yi, Z., Calandra, R., Veiga, F., van Hoof, H., Hermans, T., Zhang, Y., Peters, J.

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 4925-4930, IEEE, October 2016 (conference)

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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On Version Space Compression

Ben-David, S., Urner, R.

Algorithmic Learning Theory - 27th International Conference (ALT), 9925, pages: 50-64, Lecture Notes in Computer Science, (Editors: Ortner, R., Simon, H. U., and Zilles, S.), September 2016 (conference)

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Contextual Policy Search for Linear and Nonlinear Generalization of a Humanoid Walking Controller

Abdolmaleki, A., Lau, N., Reis, L., Peters, J., Neumann, G.

Journal of Intelligent & Robotic Systems, 83(3-4):393-408, (Editors: Luis Almeida, Lino Marques ), September 2016, Special Issue: Autonomous Robot Systems (article)

DOI [BibTex]

DOI [BibTex]


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Learning Probabilistic Features from EMG Data for Predicting Knee Abnormalities

Kohlschuetter, J., Peters, J., Rueckert, E.

XIV Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON), pages: 668-672, (Editors: Kyriacou, E., Christofides, S., and Pattichis, C. S.), September 2016 (conference)

DOI [BibTex]

DOI [BibTex]


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Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes

Grau-Moya, J, Leibfried, F, Genewein, T, Braun, DA

Machine Learning and Knowledge Discovery in Databases, pages: 475-491, Lecture Notes in Computer Science; 9852, Springer, Cham, Switzerland, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD), September 2016 (conference)

Abstract
Information-theoretic principles for learning and acting have been proposed to solve particular classes of Markov Decision Problems. Mathematically, such approaches are governed by a variational free energy principle and allow solving MDP planning problems with information-processing constraints expressed in terms of a Kullback-Leibler divergence with respect to a reference distribution. Here we consider a generalization of such MDP planners by taking model uncertainty into account. As model uncertainty can also be formalized as an information-processing constraint, we can derive a unified solution from a single generalized variational principle. We provide a generalized value iteration scheme together with a convergence proof. As limit cases, this generalized scheme includes standard value iteration with a known model, Bayesian MDP planning, and robust planning. We demonstrate the benefits of this approach in a grid world simulation.

DOI [BibTex]

DOI [BibTex]


Depth Estimation Through a Generative Model of Light Field Synthesis
Depth Estimation Through a Generative Model of Light Field Synthesis

Sajjadi, M. S. M., Köhler, R., Schölkopf, B., Hirsch, M.

Pattern Recognition - 38th German Conference (GCPR), 9796, pages: 426-438, Lecture Notes in Computer Science, (Editors: Rosenhahn, B. and Andres, B.), Springer International Publishing, September 2016 (conference)

Arxiv Project link (url) DOI [BibTex]

Arxiv Project link (url) DOI [BibTex]


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Bidirektionale Interaktion zwischen Mensch und Roboter beim Bewegungslernen (BIMROB)

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

11. Symposium der DVS Sportinformatik, September 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]


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A Low-cost Sensor Glove with Vibrotactile Feedback and Multiple Finger Joint and Hand Motion Sensing for Human-Robot Interaction

Weber, P., Rueckert, E., Calandra, R., Peters, J., Beckerle, P.

25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pages: 99-104, August 2016 (conference)

DOI [BibTex]

DOI [BibTex]