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2020


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Kernel Conditional Moment Test via Maximum Moment Restriction

Muandet, K., Jitkrittum, W., Kübler, J. M.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), August 2020 (conference) Accepted

ei

[BibTex]

2020


[BibTex]


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Bayesian Online Prediction of Change Points

Agudelo-España, D., Gomez-Gonzalez, S., Bauer, S., Schölkopf, B., Peters, J.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), August 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


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Algorithmic Recourse: from Counterfactual Explanations to Interventions

Karimi, A., Schölkopf, B., Valera, I.

37th International Conference on Machine Learning (ICML), July 2020 (conference) Submitted

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[BibTex]

[BibTex]


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Model-Agnostic Counterfactual Explanations for Consequential Decisions

Karimi, A., Barthe, G., Balle, B., Valera, I.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), June 2020 (conference) Accepted

ei plg

arXiv [BibTex]

arXiv [BibTex]


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A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization

F Alimisis, F., Orvieto, A., Becigneul, G., Lucchi, A.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), June 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


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Kernel Conditional Density Operators

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

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Proceedings of Machine Learning Research, June 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


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Where Does It End? - Reasoning About Hidden Surfaces by Object Intersection Constraints

Strecke, M., Stückler, J.

In Proceedings IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, June 2020 (inproceedings)

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preprint project page [BibTex]

preprint project page [BibTex]


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A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control

Zhu, J., Diehl, M., Schölkopf, B.

2nd Annual Conference on Learning for Dynamics and Control (L4DC), June 2020 (conference) Accepted

ei

arXiv [BibTex]

arXiv [BibTex]


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Disentangling Factors of Variations Using Few Labels

Locatello, F., Tschannen, M., Bauer, S., Rätsch, G., Schölkopf, B., Bachem, O.

8th International Conference on Learning Representations (ICLR), April 2020 (conference)

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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Mixed-curvature Variational Autoencoders

Skopek, O., Ganea, O., Becigneul, G.

8th International Conference on Learning Representations (ICLR), April 2020 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Non-linear interlinkages and key objectives amongst the Paris Agreement and the Sustainable Development Goals
Non-linear interlinkages and key objectives amongst the Paris Agreement and the Sustainable Development Goals

Laumann, F., von Kügelgen, J., Barahona, M.

ICLR 2020 Workshop "Tackling Climate Change with Machine Learning", April 2020 (conference)

ei

arXiv PDF [BibTex]

arXiv PDF [BibTex]


From Variational to Deterministic Autoencoders
From Variational to Deterministic Autoencoders

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

8th International Conference on Learning Representations (ICLR) , April 2020, *equal contribution (conference) Accepted

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.

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arXiv [BibTex]

arXiv [BibTex]


Towards causal generative scene models via competition of experts
Towards causal generative scene models via competition of experts

von Kügelgen*, J., Ustyuzhaninov*, I., Gehler, P., Bethge, M., Schölkopf, B.

ICLR 2020 Workshop "Causal Learning for Decision Making", April 2020, *equal contribution (conference)

ei

arXiv PDF [BibTex]

arXiv PDF [BibTex]


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On Mutual Information Maximization for Representation Learning

Tschannen, M., Djolonga, J., Rubenstein, P. K., Gelly, S., Lucic, M.

8th International Conference on Learning Representations (ICLR), April 2020 (conference)

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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More Powerful Selective Kernel Tests for Feature Selection

Lim, J. N., Yamada, M., Jitkrittum, W., Terada, Y., Matsui, S., Shimodaira, H.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020 (conference) To be published

ei

arXiv [BibTex]

arXiv [BibTex]


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Computationally Tractable Riemannian Manifolds for Graph Embeddings

Cruceru, C., Becigneul, G., Ganea, O.

37th International Conference on Machine Learning (ICML), 2020 (conference) Submitted

ei

[BibTex]

[BibTex]


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A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models

Agudelo-España, D., Zadaianchuk, A., Wenk, P., Garg, A., Akpo, J., Grimminger, F., Viereck, J., Naveau, M., Righetti, L., Martius, G., Krause, A., Schölkopf, B., Bauer, S., Wüthrich, M.

IEEE International Conference on Robotics and Automation (ICRA), 2020 (conference) Accepted

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Project Page PDF [BibTex]

Project Page PDF [BibTex]


Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem
Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem

Zhu, J., Jitkrittum, W., Diehl, M., Schölkopf, B.

In 59th IEEE Conference on Decision and Control (CDC), 2020 (inproceedings) Accepted

ei

[BibTex]

[BibTex]


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Practical Accelerated Optimization on Riemannian Manifolds

F Alimisis, F., Orvieto, A., Becigneul, G., Lucchi, A.

37th International Conference on Machine Learning (ICML), 2020 (conference) Submitted

ei

[BibTex]

[BibTex]


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Fair Decisions Despite Imperfect Predictions

Kilbertus, N., Gomez Rodriguez, M., Schölkopf, B., Muandet, K., Valera, I.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020 (conference) Accepted

ei plg

[BibTex]

[BibTex]


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Planning from Images with Deep Latent Gaussian Process Dynamics

Bosch, N., Achterhold, J., Leal-Taixe, L., Stückler, J.

Proceedings of the 2nd Conference on Learning for Dynamics and Control (L4DC), 120, pages: 640-650, Proceedings of Machine Learning Research (PMLR), (Editors: Alexandre M. Bayen and Ali Jadbabaie and George Pappas and Pablo A. Parrilo and Benjamin Recht and Claire Tomlin and Melanie Zeilinger), 2020, arXiv:2005.03770 (conference)

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preprint project page poster [BibTex]

preprint project page poster [BibTex]


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Constant Curvature Graph Convolutional Networks

Bachmann*, G., Becigneul*, G., Ganea, O.

37th International Conference on Machine Learning (ICML), 2020, *equal contribution (conference) Submitted

ei

[BibTex]

[BibTex]


Optimizing Rank-based Metrics with Blackbox Differentiation
Optimizing Rank-based Metrics with Blackbox Differentiation

Rolinek, M., Musil, V., Paulus, A., Vlastelica, M., Michaelis, C., Martius, G.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020, Best paper nomination (inproceedings)

Abstract
Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models. Despite years of effort, direct optimization for these metrics remains a challenge due to their non-differentiable and non-decomposable nature. We present an efficient, theoretically sound, and general method for differentiating rank-based metrics with mini-batch gradient descent. In addition, we address optimization instability and sparsity of the supervision signal that both arise from using rank-based metrics as optimization targets. Resulting losses based on recall and Average Precision are applied to image retrieval and object detection tasks. We obtain performance that is competitive with state-of-the-art on standard image retrieval datasets and consistently improve performance of near state-of-the-art object detectors.

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Code Long Oral Short Oral Arxiv Project Page [BibTex]

Code Long Oral Short Oral Arxiv Project Page [BibTex]


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DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation

Wang, R., Yang, N., Stückler, J., Cremers, D.

In Accepted for IEEE international Conference on Robotics and Automation (ICRA), 2020, arXiv:1904.10097 (inproceedings) Accepted

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[BibTex]

[BibTex]


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Divide-and-Conquer Monte Carlo Tree Search for goal directed planning

Parascandolo*, G., Buesing*, L., Merel, J., Hasenclever, L., Aslanides, J., Hamrick, J. B., Heess, N., Neitz, A., Weber, T.

2020, *equal contribution (conference) Submitted

ei

arXiv [BibTex]

arXiv [BibTex]

2017


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Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning

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

Advances in Neural Information Processing Systems 30, pages: 3849-3858, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) Project Page [BibTex]

2017


link (url) Project Page [BibTex]


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Boosting Variational Inference: an Optimization Perspective

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

Workshop: Advances in Approximate Bayesian Inference at the 31st Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Learning Independent Causal Mechanisms

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

Workshop: Learning Disentangled Representations: from Perception to Control at the 31st Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Avoiding Discrimination through Causal Reasoning

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

Advances in Neural Information Processing Systems 30, pages: 656-666, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees

Locatello, F., Tschannen, M., Rätsch, G., Jaggi, M.

Advances in Neural Information Processing Systems 30, pages: 773-784, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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AdaGAN: Boosting Generative Models

Tolstikhin, I., Gelly, S., Bousquet, O., Simon-Gabriel, C. J., Schölkopf, B.

Advances in Neural Information Processing Systems 30, pages: 5424-5433, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)

ei

arXiv link (url) Project Page [BibTex]

arXiv link (url) Project Page [BibTex]


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Safe Adaptive Importance Sampling

Stich, S. U., Raj, A., Jaggi, M.

Advances in Neural Information Processing Systems 30, pages: 4384-4394, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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ConvWave: Searching for Gravitational Waves with Fully Convolutional Neural Nets

Gebhard, T., Kilbertus, N., Parascandolo, G., Harry, I., Schölkopf, B.

Workshop on Deep Learning for Physical Sciences (DLPS) at the 31st Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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From Parity to Preference-based Notions of Fairness in Classification

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

Advances in Neural Information Processing Systems 30, pages: 229-239, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Discriminative k-shot learning using probabilistic models

Bauer*, M., Rojas-Carulla*, M., Świątkowski, J. B., Schölkopf, B., Turner, R. E.

Second Workshop on Bayesian Deep Learning at the 31st Conference on Neural Information Processing Systems , December 2017, *equal contribution (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Closed-form Inference and Prediction in Gaussian Process State-Space Models

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

Time Series Workshop at the 31st Conference on Neural Information Processing Systems, December 2017 (conference)

ei

PDF [BibTex]

PDF [BibTex]


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Learning Robust Video Synchronization without Annotations

Wieschollek, P., Freeman, I., Lensch, H. P. A.

16th IEEE International Conference on Machine Learning and Applications (ICMLA), pages: 92 - 100, (Editors: X. Chen, B. Luo, F. Luo, V. Palade, and M. A. Wani), IEEE, December 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Optimizing human learning

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

Workshop on Teaching Machines, Robots, and Humans at the 31st Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) [BibTex]

link (url) [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.

Workshop on Prioritising Online Content at the 31st Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals

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

Proceedings of the 1st Annual Conference on Robot Learning (CoRL), pages: 167-174, Proceedings of Machine Learning Research, (Editors: Sergey Levine, Vincent Vanhoucke and Ken Goldberg), PMLR, November 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Efficient Online Adaptation with Stochastic Recurrent Neural Networks

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

IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pages: 198-204, IEEE, November 2017 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows

Huang, B., Zhang, K., Zhang, J., Sanchez-Romero, R., Glymour, C., Schölkopf, B.

IEEE 17th International Conference on Data Mining (ICDM), pages: 913-918, (Editors: Vijay Raghavan,Srinivas Aluru, George Karypis, Lucio Miele and Xindong Wu), November 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Learning inverse dynamics models in O(n) time with LSTM networks

Rueckert, E., Nakatenus, M., Tosatto, S., Peters, J.

IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pages: 811-816, IEEE, November 2017 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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A Comparison of Distance Measures for Learning Nonparametric Motor Skill Libraries

Stark, S., Peters, J., Rueckert, E.

IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pages: 624-630, IEEE, November 2017 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Simulation of the underactuated Sake Robotics Gripper in V-REP

Thiem, S., Stark, S., Tanneberg, D., Peters, J., Rueckert, E.

Workshop at the International Conference on Humanoid Robots (HUMANOIDS), November 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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End-to-End Learning for Image Burst Deblurring

Wieschollek, P., Schölkopf, B., Lensch, H. P. A., Hirsch, M.

Computer Vision - ACCV 2016 - 13th Asian Conference on Computer Vision, 10114, pages: 35-51, Image Processing, Computer Vision, Pattern Recognition, and Graphics, (Editors: Lai, S.-H., Lepetit, V., Nishino, K., and Sato, Y. ), Springer, November 2017 (conference)

ei

[BibTex]

[BibTex]


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Active Incremental Learning of Robot Movement Primitives

Maeda, G., Ewerton, M., Osa, T., Busch, B., Peters, J.

Proceedings of the 1st Annual Conference on Robot Learning (CoRL), 78, pages: 37-46, Proceedings of Machine Learning Research, (Editors: Sergey Levine, Vincent Vanhoucke and Ken Goldberg), PMLR, November 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Online Video Deblurring via Dynamic Temporal Blending Network

Kim, T. H., Lee, K. M., Schölkopf, B., Hirsch, M.

Proceedings IEEE International Conference on Computer Vision (ICCV), pages: 4038-4047, IEEE, Piscataway, NJ, USA, IEEE International Conference on Computer Vision (ICCV), October 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]