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2018


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Enhancing the Accuracy and Fairness of Human Decision Making

Valera, I., Singla, A., Gomez Rodriguez, M.

Advances in Neural Information Processing Systems 31, pages: 1774-1783, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32th Annual Conference on Neural Information Processing Systems, December 2018 (conference)

ei

arXiv link (url) Project Page [BibTex]

2018


arXiv link (url) Project Page [BibTex]


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Non-factorised Variational Inference in Dynamical Systems

Ialongo, A. D., Van Der Wilk, M., Hensman, J., Rasmussen, C. E.

1st Symposion on Advances in Approximate Bayesian Inference, December 2018 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Boosting Black Box Variational Inference

Locatello*, F., Dresdner*, G., R., K., Valera, I., Rätsch, G.

Advances in Neural Information Processing Systems 31, pages: 3405-3415, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32th Annual Conference on Neural Information Processing Systems, December 2018, *equal contribution (conference)

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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Consolidating the Meta-Learning Zoo: A Unifying Perspective as Posterior Predictive Inference

Gordon*, J., Bronskill*, J., Bauer*, M., Nowozin, S., Turner, R. E.

Workshop on Meta-Learning (MetaLearn 2018) at the 32nd Conference on Neural Information Processing Systems, December 2018, *equal contribution (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Versa: Versatile and Efficient Few-shot Learning

Gordon*, J., Bronskill*, J., Bauer*, M., Nowozin, S., Turner, R. E.

Third Workshop on Bayesian Deep Learning at the 32nd Conference on Neural Information Processing Systems, December 2018, *equal contribution (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning

Harder, F., Köhler, J., Welling, M., Park, M.

Workshop on Privacy Preserving Machine Learning at the 32nd Conference on Neural Information Processing Systems, December 2018 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Learning Invariances using the Marginal Likelihood

van der Wilk, M., Bauer, M., John, S. T., Hensman, J.

Advances in Neural Information Processing Systems 31, pages: 9960-9970, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32th Annual Conference on Neural Information Processing Systems, December 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems

Mehrjou, A., Schölkopf, B.

Workshop: Infer to Control: Probabilistic Reinforcement Learning and Structured Control at the 32nd Conference on Neural Information Processing Systems, December 2018 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Resampled Priors for Variational Autoencoders

Bauer, M., Mnih, A.

Third Workshop on Bayesian Deep Learning at the 32nd Conference on Neural Information Processing Systems, December 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Generalisation in humans and deep neural networks

Geirhos, R., Temme, C. R. M., Rauber, J., Schütt, H., Bethge, M., Wichmann, F. A.

Advances in Neural Information Processing Systems 31, pages: 7549-7561, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32th Annual Conference on Neural Information Processing Systems, December 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Data-Efficient Hierarchical Reinforcement Learning

Nachum, O., Gu, S., Lee, H., Levine, S.

Advances in Neural Information Processing Systems 31, pages: 3307-3317, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32th Annual Conference on Neural Information Processing Systems, December 2018 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


Thumb xl 2018 prd
Assessing Generative Models via Precision and Recall

Sajjadi, M. S. M., Bachem, O., Lucic, M., Bousquet, O., Gelly, S.

Advances in Neural Information Processing Systems 31, pages: 5234-5243, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32th Annual Conference on Neural Information Processing Systems, December 2018 (conference)

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models

Neitz, A., Parascandolo, G., Bauer, S., Schölkopf, B.

Advances in Neural Information Processing Systems 31, pages: 9838-9848, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32th Annual Conference on Neural Information Processing Systems, December 2018 (conference)

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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Parallel and functionally segregated processing of task phase and conscious content in the prefrontal cortex

Kapoor, V., Besserve, M., Logothetis, N. K., Panagiotaropoulos, T. I.

Communications Biology, 1(215):1-12, December 2018 (article)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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A Computational Camera with Programmable Optics for Snapshot High Resolution Multispectral Imaging

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

Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, December 2018 (conference) Accepted

ei

[BibTex]

[BibTex]


Thumb xl unbenannte pr%c3%a4sentation 1
Efficient Encoding of Dynamical Systems through Local Approximations

Solowjow, F., Mehrjou, A., Schölkopf, B., Trimpe, S.

In Proceedings of the 57th IEEE International Conference on Decision and Control (CDC), pages: 6073 - 6079 , Miami, Fl, USA, December 2018 (inproceedings)

ei ics

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


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Informative Features for Model Comparison

Jitkrittum, W., Kanagawa, H., Sangkloy, P., Hays, J., Schölkopf, B., Gretton, A.

Advances in Neural Information Processing Systems 31, pages: 816-827, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32th Annual Conference on Neural Information Processing Systems, December 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds)

Groh*, F., Wieschollek*, P., Lensch, H. P. A.

Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, December 2018, *equal contribution (conference) Accepted

ei

[BibTex]

[BibTex]


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Bayesian Nonparametric Hawkes Processes

Kapoor, J., Vergari, A., Gomez Rodriguez, M., Valera, I.

Bayesian Nonparametrics workshop at the 32nd Conference on Neural Information Processing Systems, December 2018 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Regularizing Reinforcement Learning with State Abstraction

Akrour, R., Veiga, F., Peters, J., Neuman, G.

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2018 (conference) Accepted

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Learning to Categorize Bug Reports with LSTM Networks

Gondaliya, K., Peters, J., Rueckert, E.

Proceedings of the 10th International Conference on Advances in System Testing and Validation Lifecycle (VALID), pages: 7-12, October 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Domain Randomization for Simulation-Based Policy Optimization with Transferability Assessment

Muratore, F., Treede, F., Gienger, M., Peters, J.

2nd Annual Conference on Robot Learning (CoRL), 87, pages: 700-713, Proceedings of Machine Learning Research, PMLR, October 2018 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Reinforcement Learning of Phase Oscillators for Fast Adaptation to Moving Targets

Maeda, G., Koc, O., Morimoto, J.

Proceedings of The 2nd Conference on Robot Learning (CoRL), 87, pages: 630-640, (Editors: Aude Billard, Anca Dragan, Jan Peters, Jun Morimoto ), PMLR, October 2018 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


Thumb xl screen shot 2019 01 07 at 12.05.00
Control of Musculoskeletal Systems using Learned Dynamics Models

Büchler, D., Calandra, R., Schölkopf, B., Peters, J.

IEEE Robotics and Automation Letters, Robotics and Automation Letters, 3(4):3161-3168, IEEE, 2018 (article)

Abstract
Controlling musculoskeletal systems, especially robots actuated by pneumatic artificial muscles, is a challenging task due to nonlinearities, hysteresis effects, massive actuator de- lay and unobservable dependencies such as temperature. Despite such difficulties, muscular systems offer many beneficial prop- erties to achieve human-comparable performance in uncertain and fast-changing tasks. For example, muscles are backdrivable and provide variable stiffness while offering high forces to reach high accelerations. In addition, the embodied intelligence deriving from the compliance might reduce the control demands for specific tasks. In this paper, we address the problem of how to accurately control musculoskeletal robots. To address this issue, we propose to learn probabilistic forward dynamics models using Gaussian processes and, subsequently, to employ these models for control. However, Gaussian processes dynamics models cannot be set-up for our musculoskeletal robot as for traditional motor- driven robots because of unclear state composition etc. We hence empirically study and discuss in detail how to tune these approaches to complex musculoskeletal robots and their specific challenges. Moreover, we show that our model can be used to accurately control an antagonistic pair of pneumatic artificial muscles for a trajectory tracking task while considering only one- step-ahead predictions of the forward model and incorporating model uncertainty.

ei

RAL18final link (url) DOI Project Page [BibTex]

RAL18final link (url) DOI Project Page [BibTex]


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Constraint-Space Projection Direct Policy Search

Akrour, R., Peters, J., Neuman, G.

14th European Workshop on Reinforcement Learning (EWRL), October 2018 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Spatio-temporal Transformer Network for Video Restoration

Kim, T. H., Sajjadi, M. S. M., Hirsch, M., Schölkopf, B.

15th European Conference on Computer Vision (ECCV), Part III, 11207, pages: 111-127, Lecture Notes in Computer Science, (Editors: Vittorio Ferrari, Martial Hebert,Cristian Sminchisescu and Yair Weiss), Springer, September 2018 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Separating Reflection and Transmission Images in the Wild

Wieschollek, P., Gallo, O., Gu, J., Kautz, J.

European Conference on Computer Vision (ECCV), September 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Risk-Sensitivity in Simulation Based Online Planning

Schmid, K., Belzner, L., Kiermeier, M., Neitz, A., Phan, T., Gabor, T., Linnhoff, C.

KI 2018: Advances in Artificial Intelligence - 41st German Conference on AI, pages: 229-240, (Editors: F. Trollmann and A. Y. Turhan), Springer, Cham, September 2018 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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The Unreasonable Effectiveness of Texture Transfer for Single Image Super-resolution

Gondal, M. W., Schölkopf, B., Hirsch, M.

Workshop and Challenge on Perceptual Image Restoration and Manipulation (PIRM) at the 15th European Conference on Computer Vision (ECCV), September 2018 (conference)

ei

arXiv URL [BibTex]

arXiv URL [BibTex]


Thumb xl grasping
Leveraging Contact Forces for Learning to Grasp

Merzic, H., Bogdanovic, M., Kappler, D., Righetti, L., Bohg, J.

arXiv, September 2018, Submitted to ICRA'19 (article) Submitted

Abstract
Grasping objects under uncertainty remains an open problem in robotics research. This uncertainty is often due to noisy or partial observations of the object pose or shape. To enable a robot to react appropriately to unforeseen effects, it is crucial that it continuously takes sensor feedback into account. While visual feedback is important for inferring a grasp pose and reaching for an object, contact feedback offers valuable information during manipulation and grasp acquisition. In this paper, we use model-free deep reinforcement learning to synthesize control policies that exploit contact sensing to generate robust grasping under uncertainty. We demonstrate our approach on a multi-fingered hand that exhibits more complex finger coordination than the commonly used two- fingered grippers. We conduct extensive experiments in order to assess the performance of the learned policies, with and without contact sensing. While it is possible to learn grasping policies without contact sensing, our results suggest that contact feedback allows for a significant improvement of grasping robustness under object pose uncertainty and for objects with a complex shape.

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video arXiv [BibTex]

video arXiv [BibTex]


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From Deterministic ODEs to Dynamic Structural Causal Models

Rubenstein, P. K., Bongers, S., Schölkopf, B., Mooij, J. M.

Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI), August 2018 (conference)

ei

Arxiv link (url) [BibTex]

Arxiv link (url) [BibTex]


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Generalized Score Functions for Causal Discovery

Huang, B., Zhang, K., Lin, Y., Schölkopf, B., Glymour, C.

Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages: 1551-1560, (Editors: Yike Guo and Faisal Farooq), ACM, August 2018 (conference)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming

Yurtsever, A., Fercoq, O., Locatello, F., Cevher, V.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 5713-5722, 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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Blind Justice: Fairness with Encrypted Sensitive Attributes

Kilbertus, N., Gascon, A., Kusner, M., Veale, M., Gummadi, K., Weller, A.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 2635-2644, 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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Detecting non-causal artifacts in multivariate linear regression models

Janzing, D., Schölkopf, B.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 2250-2258, 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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Learning-based solution to phase error correction in T2*-weighted GRE scans

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

1st International conference on Medical Imaging with Deep Learning (MIDL), July 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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The Mirage of Action-Dependent Baselines in Reinforcement Learning

Tucker, G., Bhupatiraju, S., Gu, S., Turner, R., Ghahramani, Z., Levine, S.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 5022-5031, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)

ei

PDF link (url) Project Page [BibTex]

PDF link (url) Project Page [BibTex]


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Intrinsic disentanglement: an invariance view for deep generative models

Besserve, M., Sun, R., Schölkopf, B.

Workshop on Theoretical Foundations and Applications of Deep Generative Models at ICML, July 2018 (conference)

ei

PDF [BibTex]

PDF [BibTex]


Thumb xl 2018 prd
Assessing Generative Models via Precision and Recall

Sajjadi, M. S. M., Bachem, O., Lucic, M., Bousquet, O., Gelly, S.

Workshop on Theoretical Foundations and Applications of Deep Generative Models (TADGM) at the 35th International Conference on Machine Learning (ICML), July 2018 (conference)

ei

arXiv [BibTex]

arXiv [BibTex]


Thumb xl 2018 tgan
Tempered Adversarial Networks

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

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 4448-4456, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos

Parmas, P., Rasmussen, C., Peters, J., Doya, K.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 4065-4074, 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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Learning Independent Causal Mechanisms

Parascandolo, G., Kilbertus, N., Rojas-Carulla, M., Schölkopf, B.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 4033-4041, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Thumb xl mazen
Robust Physics-based Motion Retargeting with Realistic Body Shapes

Borno, M. A., Righetti, L., Black, M. J., Delp, S. L., Fiume, E., Romero, J.

Computer Graphics Forum, 37, pages: 6:1-12, July 2018 (article)

Abstract
Motion capture is often retargeted to new, and sometimes drastically different, characters. When the characters take on realistic human shapes, however, we become more sensitive to the motion looking right. This means adapting it to be consistent with the physical constraints imposed by different body shapes. We show how to take realistic 3D human shapes, approximate them using a simplified representation, and animate them so that they move realistically using physically-based retargeting. We develop a novel spacetime optimization approach that learns and robustly adapts physical controllers to new bodies and constraints. The approach automatically adapts the motion of the mocap subject to the body shape of a target subject. This motion respects the physical properties of the new body and every body shape results in a different and appropriate movement. This makes it easy to create a varied set of motions from a single mocap sequence by simply varying the characters. In an interactive environment, successful retargeting requires adapting the motion to unexpected external forces. We achieve robustness to such forces using a novel LQR-tree formulation. We show that the simulated motions look appropriate to each character’s anatomy and their actions are robust to perturbations.

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pdf video Project Page Project Page [BibTex]

pdf video Project Page Project Page [BibTex]


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Nonstationary GANs: Analysis as Nonautonomous Dynamical Systems

Mehrjou, A., Schölkopf, B.

Workshop on Theoretical Foundations and Applications of Deep Generative Models at ICML, July 2018 (conference)

ei

PDF [BibTex]

PDF [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) [BibTex]

link (url) [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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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 [BibTex]

link (url) 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 [BibTex]

DOI [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]