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2018


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Probabilistic Recurrent State-Space Models

Doerr, A., Daniel, C., Schiegg, M., Nguyen-Tuong, D., Schaal, S., Toussaint, M., Trimpe, S.

In Proceedings of the International Conference on Machine Learning (ICML), International Conference on Machine Learning (ICML), July 2018 (inproceedings)

Abstract
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g., LSTMs) proved extremely successful in modeling complex time-series data. Fully probabilistic SSMs, however, unfortunately often prove hard to train, even for smaller problems. To overcome this limitation, we propose a scalable initialization and training algorithm based on doubly stochastic variational inference and Gaussian processes. In the variational approximation we propose in contrast to related approaches to fully capture the latent state temporal correlations to allow for robust training.

am ics

arXiv pdf Project Page [BibTex]

2018


arXiv pdf Project Page [BibTex]


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Hyperbolic Entailment Cones for Learning Hierarchical Embeddings

Ganea, O., Becigneul, G., Hofmann, T.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 1646-1655, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Differentially Private Database Release via Kernel Mean Embeddings

Balog, M., Tolstikhin, I., Schölkopf, B.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 423-431, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)

ei

link (url) Project Page Project Page [BibTex]

link (url) Project Page Project Page [BibTex]


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On Matching Pursuit and Coordinate Descent

Locatello, F., Raj, A., Praneeth Karimireddy, S., Rätsch, G., Schölkopf, B., Stich, S. U., Jaggi, M.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 3204-3213, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Iterative Model-Fitting and Local Controller Optimization - Towards a Better Understanding of Convergence Properties

Wüthrich, M., Schölkopf, B.

Workshop on Prediction and Generative Modeling in Reinforcement Learning at ICML, July 2018 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference

Muandet, K., Kanagawa, M., Saengkyongam, S., Marukata, S.

Workshop on Machine Learning for Causal Inference, Counterfactual Prediction, and Autonomous Action (CausalML) at ICML, July 2018 (conference)

ei

[BibTex]

[BibTex]


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Event-triggered Learning for Resource-efficient Networked Control

Solowjow, F., Baumann, D., Garcke, J., Trimpe, S.

In Proceedings of the American Control Conference (ACC), pages: 6506 - 6512, American Control Conference, June 2018 (inproceedings)

ics

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


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Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

Ruiz, F. J. R., Valera, I., Svensson, L., Perez-Cruz, F.

IEEE Transactions on Cognitive Communications and Networking, 4(2):177-191, June 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


Thumb xl 2017 frvsr
Frame-Recurrent Video Super-Resolution

Sajjadi, M. S. M., Vemulapalli, R., Brown, M.

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018 (conference)

ei

ArXiv link (url) [BibTex]

ArXiv link (url) [BibTex]


Thumb xl screen shot 2018 03 22 at 10.40.47 am
Oncilla robot: a versatile open-source quadruped research robot with compliant pantograph legs

Sproewitz, A., Tuleu, A., Ajallooeian, M., Vespignani, M., Moeckel, R., Eckert, P., D’Haene, M., Degrave, J., Nordmann, A., Schrauwen, B., Steil, J., Ijspeert, A. J.

Frontiers in Robotics and AI, 5(67), June 2018, arXiv: 1803.06259 (article)

Abstract
We present Oncilla robot, a novel mobile, quadruped legged locomotion machine. This large-cat sized, 5.1 robot is one of a kind of a recent, bioinspired legged robot class designed with the capability of model-free locomotion control. Animal legged locomotion in rough terrain is clearly shaped by sensor feedback systems. Results with Oncilla robot show that agile and versatile locomotion is possible without sensory signals to some extend, and tracking becomes robust when feedback control is added (Ajaoolleian 2015). By incorporating mechanical and control blueprints inspired from animals, and by observing the resulting robot locomotion characteristics, we aim to understand the contribution of individual components. Legged robots have a wide mechanical and control design parameter space, and a unique potential as research tools to investigate principles of biomechanics and legged locomotion control. But the hardware and controller design can be a steep initial hurdle for academic research. To facilitate the easy start and development of legged robots, Oncilla-robot's blueprints are available through open-source. [...]

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link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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Learning Face Deblurring Fast and Wide

Jin, M., Hirsch, M., Favaro, P.

The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pages: 745-753, June 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Thumb xl screen shot 2018 04 18 at 11.01.27 am
Learning from Outside the Viability Kernel: Why we Should Build Robots that can Fail with Grace

Heim, S., Sproewitz, A.

Proceedings of SIMPAR 2018, pages: 55-61, IEEE, 2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), May 2018 (conference)

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link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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Wasserstein Auto-Encoders

Tolstikhin, I., Bousquet, O., Gelly, S., Schölkopf, B.

6th International Conference on Learning Representations (ICLR), May 2018 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Fidelity-Weighted Learning

Dehghani, M., Mehrjou, A., Gouws, S., Kamps, J., Schölkopf, B.

6th International Conference on Learning Representations (ICLR), May 2018 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Inducing Probabilistic Context-Free Grammars for the Sequencing of Movement Primitives

Lioutikov, R., Maeda, G., Veiga, F., Kersting, K., Peters, J.

IEEE International Conference on Robotics and Automation, (ICRA), pages: 1-8, IEEE, May 2018 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Sobolev GAN

Mroueh, Y., Li*, C., Sercu*, T., Raj*, A., Cheng, Y.

6th International Conference on Learning Representations (ICLR), May 2018, *equal contribution (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Assisting Movement Training and Execution With Visual and Haptic Feedback

Ewerton, M., Rother, D., Weimar, J., Kollegger, G., Wiemeyer, J., Peters, J., Maeda, G.

Frontiers in Neurorobotics, 12, May 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Temporal Difference Models: Model-Free Deep RL for Model-Based Control

Pong*, V., Gu*, S., Dalal, M., Levine, S.

6th International Conference on Learning Representations (ICLR), May 2018, *equal contribution (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders

Rubenstein, P. K., Schölkopf, B., Tolstikhin, I.

Workshop at the 6th International Conference on Learning Representations (ICLR), May 2018 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning

Eysenbach, B., Gu, S., Ibarz, J., Levine, S.

6th International Conference on Learning Representations (ICLR), May 2018 (conference)

ei

Videos link (url) Project Page [BibTex]

Videos link (url) Project Page [BibTex]


Thumb xl 2018 tgan
Tempered Adversarial Networks

Sajjadi, M. S. M., Parascandolo, G., Mehrjou, A., Schölkopf, B.

Workshop at the 6th International Conference on Learning Representations (ICLR), May 2018 (conference)

ei

arXiv [BibTex]

arXiv [BibTex]


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Learning Coupled Forward-Inverse Models with Combined Prediction Errors

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

IEEE International Conference on Robotics and Automation, (ICRA), pages: 2433-2439, IEEE, May 2018 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Learning Disentangled Representations with Wasserstein Auto-Encoders

Rubenstein, P. K., Schölkopf, B., Tolstikhin, I.

Workshop at the 6th International Conference on Learning Representations (ICLR), May 2018 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


Thumb xl screen shot 2018 02 03 at 9.09.06 am
Shaping in Practice: Training Wheels to Learn Fast Hopping Directly in Hardware

Heim, S., Ruppert, F., Sarvestani, A., Sproewitz, A.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2018, pages: 5076-5081, IEEE, International Conference on Robotics and Automation, May 2018 (inproceedings)

Abstract
Learning instead of designing robot controllers can greatly reduce engineering effort required, while also emphasizing robustness. Despite considerable progress in simulation, applying learning directly in hardware is still challenging, in part due to the necessity to explore potentially unstable parameters. We explore the of concept shaping the reward landscape with training wheels; temporary modifications of the physical hardware that facilitate learning. We demonstrate the concept with a robot leg mounted on a boom learning to hop fast. This proof of concept embodies typical challenges such as instability and contact, while being simple enough to empirically map out and visualize the reward landscape. Based on our results we propose three criteria for designing effective training wheels for learning in robotics.

dlg

Video Youtube link (url) Project Page [BibTex]

Video Youtube link (url) Project Page [BibTex]


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Automatic Estimation of Modulation Transfer Functions

Bauer, M., Volchkov, V., Hirsch, M., Schölkopf, B.

IEEE International Conference on Computational Photography (ICCP), May 2018 (conference)

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DOI [BibTex]

DOI [BibTex]


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Causal Discovery Using Proxy Variables

Rojas-Carulla, M., Baroni, M., Lopez-Paz, D.

Workshop at 6th International Conference on Learning Representations (ICLR), May 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Sample and Feedback Efficient Hierarchical Reinforcement Learning from Human Preferences

Pinsler, R., Akrour, R., Osa, T., Peters, J., Neumann, G.

IEEE International Conference on Robotics and Automation, (ICRA), pages: 596-601, IEEE, May 2018 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Group invariance principles for causal generative models

Besserve, M., Shajarisales, N., Schölkopf, B., Janzing, D.

Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 84, pages: 557-565, Proceedings of Machine Learning Research, (Editors: Amos Storkey and Fernando Perez-Cruz), PMLR, April 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Boosting Variational Inference: an Optimization Perspective

Locatello, F., Khanna, R., Ghosh, J., Rätsch, G.

Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 84, pages: 464-472, Proceedings of Machine Learning Research, (Editors: Amos Storkey and Fernando Perez-Cruz), PMLR, April 2018 (conference)

ei

link (url) Project Page Project Page [BibTex]

link (url) Project Page Project Page [BibTex]


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Mixture of Attractors: A Novel Movement Primitive Representation for Learning Motor Skills From Demonstrations

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

IEEE Robotics and Automation Letters, 3(2):926-933, April 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Evaluating Low-Power Wireless Cyber-Physical Systems

Baumann, D., Mager, F., Singh, H., Zimmerling, M., Trimpe, S.

In Proceedings of the IEEE Workshop on Benchmarking Cyber-Physical Networks and Systems (CPSBench), pages: 13-18, IEEE Workshop on Benchmarking Cyber-Physical Networks and Systems (CPSBench), April 2018 (inproceedings)

ics

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


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Probabilistic movement primitives under unknown system dynamics

Paraschos, A., Rueckert, E., Peters, J., Neumann, G.

Advanced Robotics, 32(6):297-310, April 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Cause-Effect Inference by Comparing Regression Errors

Blöbaum, P., Janzing, D., Washio, T., Shimizu, S., Schölkopf, B.

Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) , 84, pages: 900-909, Proceedings of Machine Learning Research, (Editors: Amos Storkey and Fernando Perez-Cruz), PMLR, April 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Will People Like Your Image? Learning the Aesthetic Space

Schwarz, K., Wieschollek, P., Lensch, H. P. A.

2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pages: 2048-2057, March 2018 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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An Algorithmic Perspective on Imitation Learning

Osa, T., Pajarinen, J., Neumann, G., Bagnell, J., Abbeel, P., Peters, J.

Foundations and Trends in Robotics, 7(1-2):1-179, March 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Using Probabilistic Movement Primitives in Robotics

Paraschos, A., Daniel, C., Peters, J., Neumann, G.

Autonomous Robots, 42(3):529-551, March 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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A kernel-based approach to learning contact distributions for robot manipulation tasks

Kroemer, O., Leischnig, S., Luettgen, S., Peters, J.

Autonomous Robots, 42(3):581-600, March 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation

Kim, J., Tabibian, B., Oh, A., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM), pages: 324-332, (Editors: Yi Chang, Chengxiang Zhai, Yan Liu, and Yoelle Maarek), ACM, Febuary 2018 (conference)

ei

DOI Project Page Project Page [BibTex]

DOI Project Page Project Page [BibTex]


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Distributed Event-Based State Estimation for Networked Systems: An LMI Approach

Muehlebach, M., Trimpe, S.

IEEE Transactions on Automatic Control, 63(1):269-276, January 2018 (article)

am ics

arXiv (extended version) DOI Project Page [BibTex]

arXiv (extended version) DOI Project Page [BibTex]


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Approximate Value Iteration Based on Numerical Quadrature

Vinogradska, J., Bischoff, B., Peters, J.

IEEE Robotics and Automation Letters, 3(2):1330-1337, January 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Biomimetic Tactile Sensors and Signal Processing with Spike Trains: A Review

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

Sensors and Actuators A: Physical, 269, pages: 41-52, January 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Impact of the AIF Recording Method on Kinetic Parameters in Small Animal PET

Napieczynska, H., Kolb, A., Katiyar, P., Tonietto, M., Ud-Dean, M., Stumm, R., Herfert, K., Calaminus, C., Pichler, B.

Journal of Nuclear Medicine, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Functional Programming for Modular Bayesian Inference

Ścibior, A., Kammar, O., Ghahramani, Z.

Proceedings of the ACM on Functional Programming (ICFP), 2(Article No. 83):1-29, ACM, 2018 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Nonclassical states of light with a smooth P function

Damanet, F., Kübler, J. M., Martin, J., Braun, D.

Physical Review A, 97(2):023832, 2018 (article)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Design and Analysis of the NIPS 2016 Review Process

Shah*, N., Tabibian*, B., Muandet, K., Guyon, I., von Luxburg, U.

Journal of Machine Learning Research, 19(49):1-34, 2018, *equal contribution (article)

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

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


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A Flexible Approach for Fair Classification

Zafar, M. B., Valera, I., Gomez Rodriguez, M., Gummadi, K.

Journal of Machine Learning, 2018 (article) Accepted

ei

Project Page [BibTex]

Project Page [BibTex]


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Automatic Bayesian Density Analysis

Vergari, A., Molina, A., Peharz, R., Ghahramani, Z., Kersting, K., Valera, I.

2018 (conference) Submitted

ei

arXiv [BibTex]

arXiv [BibTex]


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Magic Tunnels

Kilcher*, Y., Becigneul*, G., Hofmann, T.

ICLR 2019, 2018, *equal contribution (conference) Submitted

ei

[BibTex]

[BibTex]