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2020


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Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

Karimi*, A., von Kügelgen*, J., Schölkopf, B., Valera, I.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020, *equal contribution (conference) Accepted

arXiv [BibTex]

2020

arXiv [BibTex]


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Self-Paced Deep Reinforcement Learning

Klink, P., D’Eramo, C., Peters, J., Pajarinen, J.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

[BibTex]


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Probabilistic Linear Solvers for Machine Learning

Wenger, J., Hennig, P.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

[BibTex]


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Barking up the right tree: an approach to search over molecule synthesis DAGs

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

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

[BibTex]


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Learning Kernel Tests Without Data Splitting

Kübler, J., Jitkrittum, W., Schölkopf, B., Muandet, K.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

[BibTex]


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Dual Instrumental Variable Regression

Muandet, K., Mehrjou, A., Lee, S. K., Raj, A.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

[BibTex]


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A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings

Park, J., Muandet, K.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

[BibTex]


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MATE: Plugging in Model Awareness to Task Embedding for Meta Learning

Chen, X., Wang, Z., Tang, S., Muandet, K.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

[BibTex]


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Object-Centric Learning with Slot Attention

Locatello, F., Weissenborn, D., Unterthiner, T., Mahendran, A., Heigold, G., Uszkoreit, J., Dosovitskiy, A., Kipf, T.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

[BibTex]


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Relative gradient optimization of the Jacobian term in unsupervised deep learning

Gresele, L., Fissore, G., Javaloy, A., Schölkopf, B., Hyvarinen, A.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

[BibTex]


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Causal analysis of Covid-19 Spread in Germany

Mastakouri, A., Schölkopf, B.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

[BibTex]


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Modeling Shared responses in Neuroimaging Studies through MultiView ICA

Richard, H., Gresele, L., Hyvarinen, A., Thirion, B., Gramfort, A., Ablin, P.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

[BibTex]


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Stochastic Stein Discrepancies

Gorham, J., Raj, A., Mackey, L.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

[BibTex]


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Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining

Tripp, A., Daxberger, E., Hernández-Lobato, J. M.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

[BibTex]


Grasping Field: Learning Implicit Representations for Human Grasps
Grasping Field: Learning Implicit Representations for Human Grasps

(Best Paper Award)

Karunratanakul, K., Yang, J., Zhang, Y., Black, M., Muandet, K., Tang, S.

In International Conference on 3D Vision (3DV), November 2020 (inproceedings)

Abstract
Robotic grasping of house-hold objects has made remarkable progress in recent years. Yet, human grasps are still difficult to synthesize realistically. There are several key reasons: (1) the human hand has many degrees of freedom (more than robotic manipulators); (2) the synthesized hand should conform to the surface of the object; and (3) it should interact with the object in a semantically and physically plausible manner. To make progress in this direction, we draw inspiration from the recent progress on learning-based implicit representations for 3D object reconstruction. Specifically, we propose an expressive representation for human grasp modelling that is efficient and easy to integrate with deep neural networks. Our insight is that every point in a three-dimensional space can be characterized by the signed distances to the surface of the hand and the object, respectively. Consequently, the hand, the object, and the contact area can be represented by implicit surfaces in a common space, in which the proximity between the hand and the object can be modelled explicitly. We name this 3D to 2D mapping as Grasping Field, parameterize it with a deep neural network, and learn it from data. We demonstrate that the proposed grasping field is an effective and expressive representation for human grasp generation. Specifically, our generative model is able to synthesize high-quality human grasps, given only on a 3D object point cloud. The extensive experiments demonstrate that our generative model compares favorably with a strong baseline and approaches the level of natural human grasps. Furthermore, based on the grasping field representation, we propose a deep network for the challenging task of 3D hand-object interaction reconstruction from a single RGB image. Our method improves the physical plausibility of the hand-object contact reconstruction and achieves comparable performance for 3D hand reconstruction compared to state-of-the-art methods. Our model and code are available for research purpose at https://github.com/korrawe/grasping_field.

pdf arXiv code [BibTex]


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A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning

Ahmed, O.

ETH Zurich, Switzerland, October 2020 (mastersthesis)

[BibTex]


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MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware

Hohmann, M. R., Konieczny, L., Hackl, M., Wirth, B., Zaman, T., Enficiaud, R., Grosse-Wentrup, M., Schölkopf, B.

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (UIST), October 2020 (conference) Accepted

arXiv DOI [BibTex]

arXiv DOI [BibTex]


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On the Geometry of Data Representations

Bécigneul, G.

ETH Zurich, Switzerland, September 2020, (CLS Fellowship Program) (phdthesis)

[BibTex]

[BibTex]


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Beyond traditional assumptions in fair machine learning

Kilbertus, N.

University of Cambridge, UK, September 2020, (Cambridge-Tübingen-Fellowship) (phdthesis)

[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), 108, pages: 895-905, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

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), 108, pages: 820-830, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

arXiv link (url) [BibTex]

arXiv link (url) [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), 124, pages: 320-329, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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

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

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI) , 124, pages: 1-10, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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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), 124, pages: 41-50, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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On the design of consequential ranking algorithms

Tabibian, B., Gómez, V., De, A., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), 124, pages: 171-180, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Importance Sampling via Local Sensitivity

Raj, A., Musco, C., Mackey, L.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 3099-3109, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [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), 108, pages: 1297-1307, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [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), 108, pages: 277-287, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Integrals over Gaussians under Linear Domain Constraints

Gessner, A., Kanjilal, O., Hennig, P.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 2764-2774, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Modular Block-diagonal Curvature Approximations for Feedforward Architectures

Dangel, F., Harmeling, S., Hennig, P.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 799-808, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Testing Goodness of Fit of Conditional Density Models with Kernels

Jitkrittum, W., Kanagawa, H., Schölkopf, B.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), 124, pages: 221-230, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization

Negiar, G., Dresdner, G., Tsai, A. Y., El Ghaoui, L., Locatello, F., Freund, R. M., Pedregosa, F.

37th International Conference on Machine Learning (ICML), pages: 296-305, July 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Variational Autoencoders with Riemannian Brownian Motion Priors

Kalatzis, D., Eklund, D., Arvanitidis, G., Hauberg, S.

37th International Conference on Machine Learning (ICML), pages: 6789-6799, July 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Variational Bayes in Private Settings (VIPS) (Extended Abstract)

Foulds, J. R., Park, M., Chaudhuri, K., Welling, M.

Proceedings of the 29th International Joint Conference on Artificial Intelligence, (IJCAI-PRICAI), pages: 5050-5054, (Editors: Christian Bessiere), International Joint Conferences on Artificial Intelligence Organization, July 2020, Journal track (conference)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Weakly-Supervised Disentanglement Without Compromises

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

37th International Conference on Machine Learning (ICML), pages: 7753-7764, July 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks

Kristiadi, A., Hein, M., Hennig, P.

37th International Conference on Machine Learning (ICML), pages: 1226-1236, July 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Constant Curvature Graph Convolutional Networks

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

37th International Conference on Machine Learning (ICML), pages: 9118-9128, July 2020, *equal contribution (conference)

link (url) [BibTex]

link (url) [BibTex]


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Converting to Optimization in Machine Learning: Perturb-and-MAP, Differential Privacy, and Program Synthesis

Balog, M.

University of Cambridge, UK, July 2020, (Cambridge-Tübingen-Fellowship) (phdthesis)

[BibTex]

[BibTex]


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Differentiable Likelihoods for Fast Inversion of ‘Likelihood-Free’ Dynamical Systems

Kersting, H., Krämer, N., Schiegg, M., Daniel, C., Tiemann, M., Hennig, P.

37th International Conference on Machine Learning (ICML), pages: 2655-2665, July 2020 (conference)

link (url) [BibTex]

link (url) [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), 108, pages: 993-1004, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, June 2020 (conference)

link (url) [BibTex]

link (url) [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), 120, pages: 915-923, Proceedings of Machine Learning Research, (Editors: Alexandre M. Bayen and Ali Jadbabaie and George Pappas and Pablo A. Parrilo and Benjamin Recht and Claire Tomlin and Melanie Zeilinger), PMLR, June 2020 (conference)

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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Deep learning for the parameter estimation of tight-binding Hamiltonians

Cacioppo, A.

University of Roma, La Sapienza, Italy, May 2020 (mastersthesis)

[BibTex]

[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]


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Counterfactuals uncover the modular structure of deep generative models

Besserve, M., Mehrjou, A., Sun, R., Schölkopf, B.

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

link (url) [BibTex]

link (url) [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]