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2017


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A Phase-coded Aperture Camera with Programmable Optics

Chen, J., Hirsch, M., Heintzmann, R., Eberhardt, B., Lensch, H. P. A.

Electronic Imaging, 2017(17):70-75, 2017 (article)

ei

DOI [BibTex]

2017


DOI [BibTex]


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On Maximum Entropy and Inference

Gresele, L., Marsili, M.

Entropy, 19(12):article no. 642, 2017 (article)

ei

link (url) [BibTex]

link (url) [BibTex]


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Towards Engagement Models that Consider Individual Factors in HRI: On the Relation of Extroversion and Negative Attitude Towards Robots to Gaze and Speech During a Human-Robot Assembly Task

Ivaldi, S., Lefort, S., Peters, J., Chetouani, M., Provasi, J., Zibetti, E.

International Journal of Social Robotics, 9(1):63-86, 2017 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Non-parametric Policy Search with Limited Information Loss

van Hoof, H., Neumann, G., Peters, J.

Journal of Machine Learning Research , 18(73):1-46, 2017 (article)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Stability of Controllers for Gaussian Process Dynamics

Vinogradska, J., Bischoff, B., Nguyen-Tuong, D., Peters, J.

Journal of Machine Learning Research, 18(100):1-37, 2017 (article)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Learning Optimal Configurations for Modeling Frowning by Transcranial Electrical Stimulation

Sücker, K.

Graduate School of Neural Information Processing, Eberhard Karls Universität Tübingen, Germany, 2017 (mastersthesis)

ei

[BibTex]

[BibTex]


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SUV-quantification of physiological lung tissue in an integrated PET/MR-system: Impact of lung density and bone tissue

Seith, F., Schmidt, H., Gatidis, S., Bezrukov, I., Schraml, C., Pfannenberg, C., la Fougère, C., Nikolaou, K., Schwenzer, N.

PLOS ONE, 12(5):1-13, 2017 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Efficiency of analytical and sampling-based uncertainty propagation in intensity-modulated proton therapy

Wahl, N., Hennig, P., Wieser, H. P., Bangert, M.

Physics in Medicine & Biology, 62(14):5790-5807, 2017 (article)

Abstract
The sensitivity of intensity-modulated proton therapy (IMPT) treatment plans to uncertainties can be quantified and mitigated with robust/min-max and stochastic/probabilistic treatment analysis and optimization techniques. Those methods usually rely on sparse random, importance, or worst-case sampling. Inevitably, this imposes a trade-off between computational speed and accuracy of the uncertainty propagation. Here, we investigate analytical probabilistic modeling (APM) as an alternative for uncertainty propagation and minimization in IMPT that does not rely on scenario sampling. APM propagates probability distributions over range and setup uncertainties via a Gaussian pencil-beam approximation into moments of the probability distributions over the resulting dose in closed form. It supports arbitrary correlation models and allows for efficient incorporation of fractionation effects regarding random and systematic errors. We evaluate the trade-off between run-time and accuracy of APM uncertainty computations on three patient datasets. Results are compared against reference computations facilitating importance and random sampling. Two approximation techniques to accelerate uncertainty propagation and minimization based on probabilistic treatment plan optimization are presented. Runtimes are measured on CPU and GPU platforms, dosimetric accuracy is quantified in comparison to a sampling-based benchmark (5000 random samples). APM accurately propagates range and setup uncertainties into dose uncertainties at competitive run-times (GPU ##IMG## [http://ej.iop.org/images/0031-9155/62/14/5790/pmbaa6ec5ieqn001.gif] {$\leqslant {5}$} min). The resulting standard deviation (expectation value) of dose show average global ##IMG## [http://ej.iop.org/images/0031-9155/62/14/5790/pmbaa6ec5ieqn002.gif] {$\gamma_{{3}\% / {3}~{\rm mm}}$} pass rates between 94.2% and 99.9% (98.4% and 100.0%). All investigated importance sampling strategies provided less accuracy at higher run-times considering only a single fraction. Considering fractionation, APM uncertainty propagation and treatment plan optimization was proven to be possible at constant time complexity, while run-times of sampling-based computations are linear in the number of fractions. Using sum sampling within APM, uncertainty propagation can only be accelerated at the cost of reduced accuracy in variance calculations. For probabilistic plan optimization, we were able to approximate the necessary pre-computations within seconds, yielding treatment plans of similar quality as gained from exact uncertainty propagation. APM is suited to enhance the trade-off between speed and accuracy in uncertainty propagation and probabilistic treatment plan optimization, especially in the context of fractionation. This brings fully-fledged APM computations within reach of clinical application.

pn

link (url) [BibTex]

link (url) [BibTex]


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Absence of EEG correlates of self-referential processing depth in ALS

Fomina, T., Weichwald, S., Synofzik, M., Just, J., Schöls, L., Schölkopf, B., Grosse-Wentrup, M.

PLOS ONE, 12(6):e0180136, 2017 (article)

ei

PDF DOI [BibTex]


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Analytical probabilistic modeling of RBE-weighted dose for ion therapy

Wieser, H., Hennig, P., Wahl, N., Bangert, M.

Physics in Medicine and Biology (PMB), 62(23):8959-8982, 2017 (article)

pn

link (url) [BibTex]

link (url) [BibTex]

2016


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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)

ei

link (url) [BibTex]

2016


link (url) [BibTex]


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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)

ei

link (url) [BibTex]

link (url) [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)

ei

link (url) [BibTex]

link (url) [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)

ei

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)

ei

link (url) [BibTex]

link (url) [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)

ei

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)

ei

link (url) [BibTex]

link (url) [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)

ei

DOI [BibTex]

DOI [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)

ei

DOI [BibTex]

DOI [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)

am ei

link (url) DOI [BibTex]

link (url) DOI [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)

ei

DOI [BibTex]

DOI [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)

ei

link (url) [BibTex]

link (url) [BibTex]


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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)

am ei

final link (url) DOI [BibTex]

final link (url) DOI [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)

ei

Arxiv [BibTex]

Arxiv [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)

ei

link (url) [BibTex]

link (url) [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)

ei

DOI [BibTex]

DOI [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)

ei

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)

ei

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)

am ei

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)

ei

DOI [BibTex]

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)

ei

DOI [BibTex]

DOI [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)

ei

link (url) DOI [BibTex]

link (url) DOI [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)

ei

DOI [BibTex]

DOI [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)

ei

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)

ei

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.

ei

DOI [BibTex]

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)

ei

link (url) [BibTex]

link (url) [BibTex]


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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)

ei

Arxiv Project link (url) DOI [BibTex]

Arxiv Project link (url) DOI [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)

ei

DOI [BibTex]

DOI [BibTex]


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Experimental and causal view on information integration in autonomous agents

Geiger, P., Hofmann, K., Schölkopf, B.

Proceedings of the 6th International Workshop on Combinations of Intelligent Methods and Applications (CIMA), pages: 21-28, (Editors: Hatzilygeroudis, I. and Palade, V.), August 2016 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Manifold Gaussian Processes for Regression

Calandra, R., Peters, J., Rasmussen, C. E., Deisenroth, M. P.

International Joint Conference on Neural Networks (IJCNN), pages: 3338-3345, IEEE, July 2016 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Acquiring and Generalizing the Embodiment Mapping from Human Observations to Robot Skills

Maeda, G., Ewerton, M., Koert, D., Peters, J.

IEEE Robotics and Automation Letters, 1(2):784-791, July 2016 (article)

ei

DOI [BibTex]

DOI [BibTex]


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The Mondrian Kernel

Balog, M., Lakshminarayanan, B., Ghahramani, Z., Roy, D. M., Teh, Y. W.

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence (UAI), (Editors: Ihler, Alexander T. and Janzing, Dominik), June 2016 (conference)

ei

Arxiv link (url) [BibTex]

Arxiv link (url) [BibTex]


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Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data

Weichwald, S., Gretton, A., Schölkopf, B., Grosse-Wentrup, M.

Proceedings of the 6th International Workshop on Pattern Recognition in NeuroImaging (PRNI 2016), June 2016 (conference)

ei

PDF Arxiv Code DOI [BibTex]

PDF Arxiv Code DOI [BibTex]


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Efficient Large-scale Approximate Nearest Neighbor Search on the GPU

Wieschollek, P., Wang, O., Sorkine-Hornung, A., Lensch, H. P. A.

29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 2027 - 2035, IEEE, June 2016 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Domain Adaptation with Conditional Transferable Components

Gong, M., Zhang, K., Liu, T., Tao, D., Glymour, C., Schölkopf, B.

Proceedings of the 33nd International Conference on Machine Learning (ICML), 48, pages: 2839-2848, JMLR Workshop and Conference Proceedings, (Editors: Balcan, M.-F. and Weinberger, K. Q.), June 2016 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]