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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

[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

[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

[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

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

[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

[BibTex]

[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

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)

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)

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)

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.

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)

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)

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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Adaptation and Robust Learning of Probabilistic Movement Primitives

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

IEEE Transactions on Robotics, 36(2):366-379, IEEE, March 2020 (article)

arXiv DOI Project Page [BibTex]

arXiv DOI Project Page [BibTex]


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Real Time Trajectory Prediction Using Deep Conditional Generative Models

Gomez-Gonzalez, S., Prokudin, S., Schölkopf, B., Peters, J.

IEEE Robotics and Automation Letters, 5(2):970-976, IEEE, January 2020 (article)

arXiv DOI [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

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

[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

Project Page PDF [BibTex]

Project Page PDF [BibTex]


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An Adaptive Optimizer for Measurement-Frugal Variational Algorithms

Kübler, J. M., Arrasmith, A., Cincio, L., Coles, P. J.

Quantum, 4, pages: 263, 2020 (article)

link (url) DOI [BibTex]

link (url) DOI [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

[BibTex]

[BibTex]


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Advances in Latent Variable and Causal Models

Rubenstein, P.

University of Cambridge, UK, 2020, (Cambridge-Tuebingen-Fellowship) (phdthesis)

[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

[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

[BibTex]

[BibTex]


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Counterfactual Mean Embedding

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

Journal of Machine Learning Research, 2020 (article) Accepted

[BibTex]

[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

[BibTex]

[BibTex]


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Causal Discovery from Heterogeneous/Nonstationary Data

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

Journal of Machine Learning Research, 21(89):1-53, 2020 (article)

link (url) [BibTex]

link (url) [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

arXiv [BibTex]

arXiv [BibTex]


A machine learning route between band mapping and band structure
A machine learning route between band mapping and band structure

Xian*, R. P., Stimper*, V., Zacharias, M., Dong, S., Dendzik, M., Beaulieu, S., Schölkopf, B., Wolf, M., Rettig, L., Carbogno, C., Bauer, S., Ernstorfer, R.

2020, *equal contribution (misc)

arXiv [BibTex]

arXiv [BibTex]

2019


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Fisher Efficient Inference of Intractable Models

Liu, S., Kanamori, T., Jitkrittum, W., Chen, Y.

Advances in Neural Information Processing Systems 32 (NIPS 2019), pages: 8790-8800, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), NeurIPS, Neural Information Processing Systems 2019, December 2019 (conference)

link (url) [BibTex]

2019

link (url) [BibTex]


Semi-supervised learning, causality, and the conditional cluster assumption
Semi-supervised learning, causality, and the conditional cluster assumption

von Kügelgen, J., Mey, A., Loog, M., Schölkopf, B.

Advances in Neural Information Processing Systems 32 (NIPS 2019), NeurIPS, Neural Information Processing Systems 2019 - Workshop Do the right thing: machine learning and causal inference for improved decision making, December 2019 (conference)

Poster PDF link (url) [BibTex]

Poster PDF link (url) [BibTex]


Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks

von Kügelgen, J., Rubenstein, P. K., Schölkopf, B., Weller, A.

NeurIPS 2019 Workshop Do the right thing: machine learning and causal inference for improved decision making, NeurIPS, NeurIPS 2019 Workshop Do the right thing: machine learning and causal inference for improved decision making, December 2019 (conference)

arXiv Poster link (url) [BibTex]

arXiv Poster link (url) [BibTex]


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Selecting causal brain features with a single conditional independence test per feature

Mastakouri, A., Schölkopf, B., Janzing, D.

Advances in Neural Information Processing Systems 32, pages: 12532-12543, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Practical and Consistent Estimation of f-Divergences

Rubenstein, P. K., Bousquet, O., Djolonga, J., Riquelme, C., Tolstikhin, I.

Advances in Neural Information Processing Systems 32, pages: 4072-4082, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Invert to Learn to Invert

Putzky, P., Welling, M.

Advances in Neural Information Processing Systems 32, pages: 444-454, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

link (url) [BibTex]

link (url) [BibTex]


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On the Fairness of Disentangled Representations

Locatello, F., Abbati, G., Rainforth, T., Bauer, S., Schölkopf, B., Bachem, O.

Advances in Neural Information Processing Systems 32, pages: 14584-14597, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Limitations of the empirical Fisher approximation for natural gradient descent

Kunstner, F., Hennig, P., Balles, L.

Advances in Neural Information Processing Systems 32, pages: 4158-4169, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

link (url) [BibTex]

link (url) [BibTex]


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A Model to Search for Synthesizable Molecules

Bradshaw, J., Paige, B., Kusner, M. J., Segler, M., Hernández-Lobato, J. M.

Advances in Neural Information Processing Systems 32, pages: 7935-7947, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Kernel Stein Tests for Multiple Model Comparison

Lim, J. N., Yamada, M., Schölkopf, B., Jitkrittum, W.

Advances in Neural Information Processing Systems 32, pages: 2240-2250, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

link (url) [BibTex]

link (url) [BibTex]


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On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

Gondal, M. W., Wuthrich, M., Miladinovic, D., Locatello, F., Breidt, M., Volchkov, V., Akpo, J., Bachem, O., Schölkopf, B., Bauer, S.

Advances in Neural Information Processing Systems 32, pages: 15714-15725, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Convergence Guarantees for Adaptive Bayesian Quadrature Methods

Kanagawa, M., Hennig, P.

Advances in Neural Information Processing Systems 32, pages: 6234-6245, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Robot Learning for Muscular Systems

Büchler, D.

Technical University Darmstadt, Germany, December 2019 (phdthesis)

[BibTex]

[BibTex]


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Are Disentangled Representations Helpful for Abstract Visual Reasoning?

van Steenkiste, S., Locatello, F., Schmidhuber, J., Bachem, O.

Advances in Neural Information Processing Systems 32, pages: 14222-14235, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

link (url) [BibTex]

link (url) [BibTex]