Publications

Empirical Inference Article Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-contrast Imaging in the Presence of Non-Gaussian Noise Bonse, M. J., Garvin, E. O., Gebhard, T. D., Dannert, F. A., Cantalloube, F., Cugno, G., Absil, O., Hayoz, J., Milli, J., Kasper, M., Quanz, S. P. The American Astronomical Society, 166(2), July 2023 (Published) DOI BibTeX

Empirical Inference Robust Machine Learning Conference Paper Desiderata for Representation Learning from Identifiability, Disentanglement, and Group-Structuredness Keurti, H., Reizinger, P., Schölkopf, B., Brendel, W. 2nd Annual Topology, Algebra, and Geometry in Machine Learning (TAG) at ICML 2023, July 2023 (Published) URL BibTeX

Empirical Inference Article Dexterous robotic manipulation using deep reinforcement learning and knowledge transfer for complex sparse reward-based tasks Wang, Q., Sanchez, F. R., McCarthy, R., Bulens, D. C., McGuinness, K., O’Connor, N., Wüthrich, M., Widmaier, F., Bauer, S., Redmond, S. J. Expert Systems, 40(6), July 2023 (Published) DOI URL BibTeX

Empirical Inference Conference Paper Diffusion Based Representation Learning Mittal*, S., Abstreiter*, K., Bauer, S., Schölkopf, B., Mehrjou, A. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:24963-24982, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023, *equal contribution (Published) URL BibTeX

Empirical Inference Conference Paper Discrete Key-Value Bottleneck Träuble, F., Goyal, A., Rahaman, N., Mozer, M. C., Kawaguchi, K., Bengio, Y., Schölkopf, B. Proceedings of the 40th International Conference on Machine Learning (ICML) , 202:34431-34455, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023 (Published) URL BibTeX

Empirical Inference Conference Paper Efficient Semiring-Weighted Earley Parsing Opedal, A., Zmigrod, R., Vieira, T., Cotterell, R., Eisner, J. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), 1:3687-3713, (Editors: Anna Rogers, Jordan L. Boyd-Graber and Naoaki Okazaki), Association for Computational Linguistics, July 2023 (Published) URL BibTeX

Empirical Inference Conference Paper Estimation Beyond Data Reweighting: Kernel Method of Moments Kremer, H., Nemmour, Y., Schölkopf, B., Zhu, J. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:17745-17783, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023 (Published) URL BibTeX

Empirical Inference Conference Paper Flow Matching for Scalable Simulation-Based Inference Wildberger*, J. B., Dax*, M., Buchholz*, S., Green, S. R., Macke, J. H., Schölkopf, B. ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling, July 2023, *equal contribution (Published) URL BibTeX

Empirical Inference Optics and Sensing Laboratory Software Workshop Conference Paper Glare Removal for Astronomical Images with High Local Dynamic Range Bastelaer, M., Kremer, H., Volchkov, V., Passy, J., Schölkopf, B. IEEE International Conference on Computational Photography (ICCP), 1-11, IEEE, July 2023 (Published) DOI BibTeX

Empirical Inference Conference Paper Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning Guist, S., Schneider, J., Dittrich, A., Berenz, V., Schölkopf, B., Büchler, D. Robotics: Science and Systems XIX, July 2023 (Published) Project Page DOI URL BibTeX

Empirical Inference Conference Paper Homomorphism AutoEncoder — Learning Group Structured Representations from Observed Transitions Keurti, H., Pan, H., Besserve, M., Grewe, B. F., Schölkopf, B. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:16190-16215, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023 (Published) arXiv URL BibTeX

Empirical Inference Ph.D. Thesis Learning and Testing Powerful Hypotheses Kübler, J. M. University of Tübingen, Germany, July 2023 (Published) BibTeX

Empirical Inference Conference Paper Membership Inference Attacks against Language Models via Neighbourhood Comparison Mattern, J., Mireshghallah, F., Jin, Z., Schölkopf, B., Sachan, M., Berg-Kirkpatrick, T. Findings of the Association for Computational Linguistics (ACL), 11330-11343, (Editors: Rogers, A. and Boyd-Graber, J. L. and Okazaki, N.), Association for Computational Linguistics, July 2023 (Published) DOI BibTeX

Empirical Inference Conference Paper On Data Manifolds Entailed by Structural Causal Models Dominguez-Olmedo, R., Karimi, A., Arvanitidis, G., Schölkopf, B. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:8188-8201, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023 (Published) URL BibTeX

Empirical Inference Conference Paper On the Identifiability and Estimation of Causal Location-Scale Noise Models Immer, A., Schultheiss, C., Vogt, J. E., Schölkopf, B., Bühlmann, P., Marx, A. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:14316-14332, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023 (Published) URL BibTeX

Empirical Inference Conference Paper On the Relationship Between Explanation and Prediction: A Causal View Karimi, A., Muandet, K., Kornblith, S., Schölkopf, B., Kim, B. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:15861-15883, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023 (Published) URL BibTeX

Empirical Inference Robust Machine Learning Conference Paper Provably Learning Object-Centric Representations Brady*, J., Zimmermann*, R. S., Sharma, Y., Schölkopf, B., von Kügelen, J., Brendel, W. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:3038-3062, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), JMLR, Cambridge, MA, July 2023, *equal contribution (Published) URL BibTeX

Empirical Inference Conference Paper Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels Immer, A., van der Ouderaa, T. F. A., van der Wilk, M., Rätsch, G., Schölkopf, B. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:14333-14352, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023 (Published) URL BibTeX

Empirical Inference Conference Paper Temporal Label Smoothing for Early Event Prediction Yèche*, H., Pace*, A., Rätsch, G., Kuznetsova, R. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:39913-39938, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023, *equal contribution (Published) arXiv URL BibTeX

Empirical Inference Conference Paper The Hessian perspective into the Nature of Convolutional Neural Networks Singh, S. P., Hofmann, T., Schölkopf, B. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:31930-31968, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023 (Published) URL BibTeX

Empirical Inference Conference Paper When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP Ni, J., Jin, Z., Wang, Q., Sachan, M., Leippold, M. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), Volume 1: Long Papers:7465-7488, (Editors: Rogers, A. and Boyd-Graber, J. L. and Okazaki, N.), Association for Computational Linguistics, July 2023 (Published) DOI BibTeX

Empirical Inference Conference Paper World Models for Math Story Problems Opedal, A., Stoehr, N., Saparov, A., Sachan, M. Findings of the Association for Computational Linguistics (ACL), 9088-9115, (Editors: Anna Rogers, Jordan L. Boyd-Graber and Naoaki Okazaki), Association for Computational Linguistics, July 2023 (Published) URL BibTeX

Empirical Inference Conference Paper ALERT: Adapt Language Models to Reasoning Tasks Yu, P., Wang, T., Golovneva, O., AlKhamissi, B., Verma, S., Jin, Z., Ghosh, G., Diab, M., Celikyilmaz, A. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), 1:1055-1081, (Editors: Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki), Association for Computational Linguistics, July 2023 (Published) DOI URL BibTeX

Embodied Vision Learning and Dynamical Systems Empirical Inference Conference Paper Black-Box vs. Gray-Box: A Case Study on Learning Table Tennis Ball Trajectory Prediction with Spin and Impacts Achterhold, J., Tobuschat, P., Ma, H., Büchler, D., Muehlebach, M., Stueckler, J. In Conference on Learning for Dynamics and Control, 211:878-890, Proceedings of Machine Learning Research, (Editors: Nikolai Matni, Manfred Morari and George J. Pappa), PMLR, June 2023 (Published) preprint code URL BibTeX

Empirical Inference Conference Paper Bridging the Gap between Model Explanations in Partially Annotated Multi-label Classification Kim, Y., Kim, J. M., Jeong, J., Schmid, C., Akata, Z., Lee, J. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 3408-3417, IEEE, June 2023 (Published) DOI URL BibTeX

Empirical Inference Article Classifying the unknown: Insect identification with deep hierarchical Bayesian learning Badirli, S., Picard, C. J., Mohler, G., Richert, F., Akata, Z., Dundar, M. Methods in Ecology and Evolution, 14(6):1515-1530, June 2023 (Published) DOI BibTeX

Empirical Inference Conference Paper Editing a Woman’s Voice Costello, A., Fedorova, E., Jin, Z., Mihalcea, R. International Conference on the Science of Science and Innovation (ICSSI), June 2023 (Published) URL BibTeX

Empirical Inference Article Evaluating vaccine allocation strategies using simulation-assisted causal modeling Kekić, A., Dehning, J., Gresele, L., von Kügelgen, J., Priesemann, V., Schölkopf, B. Patterns, 4(6), June 2023 (Published) DOI URL BibTeX

Empirical Inference Ph.D. Thesis Learning Identifiable Representations: Independent Influences and Multiple Views Gresele, L. University of Tübingen, Germany, June 2023 (Published) BibTeX

Empirical Inference Conference Paper Learning Locomotion Skills from MPC in Sensor Space Khadiv, M., Meduri, A., Zhu, H., Righetti, L., Schölkopf, B. Proceedings of The 5th Annual Learning for Dynamics and Control Conference (L4DC), 211:1218-1230, (Editors: Matni, Nikolai and Morari, Manfred and Pappas, George J.), PMLR, June 2023 (Published) URL BibTeX

Empirical Inference Article Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET–MRI data Katiyar, P., Schwenck, J., Frauenfeld, L., Divine, M. R., Agrawal, V., Kohlhofer, U., Gatidis, S., Kontermann, R., Königsrainer, A., Quintanilla-Martinez, L., la Fougère, C., Schölkopf, B., Pichler, B. J., Disselhorst, J. A. Nature Biomedical Engineering, 7(8):1014-1027, June 2023 (Published) DOI BibTeX

Empirical Inference Conference Paper Robustness Implies Fairness in Causal Algorithmic Recourse Ehyaei, A., Karimi, A., Schölkopf, B., Maghsudi, S. Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT), 984-1001, ACM, June 2023 (Published) DOI BibTeX

Empirical Inference Conference Paper Workshop: Machine Learning for Science: Mathematics at the Interface of Data-driven and Mechanistic Modelling Lawrence, N., Montgomery, J., Schölkopf, B. Oberwolfach Reports, 20(2):1453-1484, June 2023 (Published) DOI URL BibTeX

Empirical Inference Conference Paper AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation Dittrich, A., Schneider, J., Guist, S., Gürtler, N., Ott, H., Steinbrenner, T., Schölkopf, B., Büchler, D. IEEE International Conference on Robotics and Automation (ICRA), 3058-3064, IEEE, June 2023 (Published) arXiv DOI BibTeX

Autonomous Learning Empirical Inference Conference Paper Benchmarking Offline Reinforcement Learning on Real-Robot Hardware Gürtler, N., Blaes, S., Kolev, P., Widmaier, F., Wüthrich, M., Bauer, S., Schölkopf, B., Martius, G. In Proceedings of the Eleventh International Conference on Learning Representations, The Eleventh International Conference on Learning Representations (ICLR), May 2023 (Published)

Abstract ›

Learning policies from previously recorded data is a promising direction for real-world robotics tasks, as online learning is often infeasible. Dexterous manipulation in particular remains an open problem in its general form. The combination of offline reinforcement learning with large diverse datasets, however, has the potential to lead to a breakthrough in this challenging domain analogously to the rapid progress made in supervised learning in recent years. To coordinate the efforts of the research community toward tackling this problem, we propose a benchmark including: i) a large collection of data for offline learning from a dexterous manipulation platform on two tasks, obtained with capable RL agents trained in simulation; ii) the option to execute learned policies on a real-world robotic system and a simulation for efficient debugging. We evaluate prominent open-sourced offline reinforcement learning algorithms on the datasets and provide a reproducible experimental setup for offline reinforcement learning on real systems.

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Autonomous Learning Empirical Inference Conference Paper DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems Schumacher, P., Haeufle, D. F., Büchler, D., Schmitt, S., Martius, G. In The Eleventh International Conference on Learning Representations (ICLR), May 2023 (Published)

Abstract ›

Muscle-actuated organisms are capable of learning an unparalleled diversity of dexterous movements despite their vast amount of muscles. Reinforcement learning (RL) on large musculoskeletal models, however, has not been able to show similar performance. We conjecture that ineffective exploration in large overactuated action spaces is a key problem. This is supported by our finding that common exploration noise strategies are inadequate in synthetic examples of overactuated systems. We identify differential extrinsic plasticity (DEP), a method from the domain of self-organization, as being able to induce state-space covering exploration within seconds of interaction. By integrating DEP into RL, we achieve fast learning of reaching and locomotion in musculoskeletal systems, outperforming current approaches in all considered tasks in sample efficiency and robustness.

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Empirical Inference Article ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning Mineeva*, O., Danciu*, D., Schölkopf, B., Ley, R. E., Rätsch, G., Youngblut, N. D. PLOS Computational Biology, 19(5), Public Library of Science, San Francisco, CA, May 2023, *equal contribution (Published) DOI BibTeX

Empirical Inference Article A Kernel Stein Test for Comparing Latent Variable Models Kanagawa, H., Jitkrittum, W., Mackey, L., Fukumizu, K., Gretton, A. Journal of the Royal Statistical Society Series B: Statistical Methodology, 85(3):986-1011, May 2023 (Published) arXiv DOI BibTeX

Empirical Inference Conference Paper A law of adversarial risk, interpolation, and label noise Paleka, D., Sanyal, A. The Eleventh International Conference on Learning Representations (ICLR), May 2023 (Published) URL BibTeX

Empirical Inference Article Better Together: Data Harmonization and Cross-StudAnalysis of Abdominal MRI Data From UK Biobank and the German National Cohort Gatidis, S., Kart, T., Fischer, M., Winzeck, S., Glocker, B., Bai, W., Bülow, R., Emmel, C., Friedrich, L., Kauczor, H., Keil, T., Kröncke, T., Mayer, P., Niendorf, T., Peters, A., Pischon, T., Schaarschmidt, B., Schmidt, B., Schulze, M., Umutle, L., et al. Investigative Radiology, 58(5):346-354, May 2023 (Published) DOI BibTeX

Autonomous Learning Empirical Inference Conference Paper Bridging the Gap to Real-World Object-Centric Learning Seitzer, M., Horn, M., Zadaianchuk, A., Zietlow, D., Xiao, T., Simon-Gabriel, C., He, T., Zhang, Z., Schölkopf, B., Brox, T., Locatello, F. In Proceedings of the Eleventh International Conference on Learning Representations, The Eleventh International Conference on Learning Representations (ICLR), May 2023 (Published)

Abstract ›

Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world. Allowing machine learning algorithms to derive this decomposition in an unsupervised way has become an important line of research. However, current methods are restricted to simulated data or require additional information in the form of motion or depth in order to successfully discover objects. In this work, we overcome this limitation by showing that reconstructing features from models trained in a self-supervised manner is a sufficient training signal for object-centric representations to arise in a fully unsupervised way. Our approach, DINOSAUR, significantly out-performs existing object-centric learning models on simulated data and is the first unsupervised object-centric model that scales to real world-datasets such as COCO and PASCAL VOC. DINOSAUR is conceptually simple and shows competitive performance compared to more involved pipelines from the computer vision literature.

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Empirical Inference Conference Paper Disentanglement of Correlated Factors via Hausdorff Factorized Support Roth, K., Ibrahim, M., Akata, Z., Vincent, P., Bouchacourt, D. The Eleventh International Conference on Learning Representations (ICLR), May 2023 (Published) URL BibTeX

Empirical Inference Article Estimating uncertainty in read-out patterns: Application to controls-based denoising and voxel-based morphometry patterns in neurodegenerative and neuropsychiatric diseases Blum, D., Hepp, T., Belov, V., Goya-Maldonado, R., la Fougère, C., Reimold, M. Human Brain Mapping, 44(7):2802-2814, May 2023 (Published) DOI BibTeX

Empirical Inference Conference Paper Flow Annealed Importance Sampling Bootstrap Midgley*, L. I., Stimper*, V., Simm, G. N. C., Schölkopf, B., Hernádez-Lobato, J. M. The Eleventh International Conference on Learning Representations (ICLR), May 2023, *equal contribution (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap Liu, W., Yu, L., Weller, A., Schölkopf, B. The Eleventh International Conference on Learning Representations (ICLR), May 2023 (Published) URL BibTeX

Empirical Inference Conference Paper How robust is unsupervised representation learning to distribution shift? Shi, Y., Daunhawer, I., Vogt, J. E., Torr, P., Sanyal, A. The Eleventh International Conference on Learning Representations (ICLR), May 2023 (Published) URL BibTeX

Empirical Inference Conference Paper Identifiability Results for Multimodal Contrastive Learning Daunhawer, I., Bizeul, A., Palumbo, E., Marx, A., Vogt, J. E. The Eleventh International Conference on Learning Representations (ICLR), May 2023 (Published) URL BibTeX

Empirical Inference Conference Paper Investigating the Impact of Action Representations in Policy Gradient Algorithms Schneider, J., Schumacher, P., Häufle, D., Schölkopf, B., Büchler, D. Workshop on effective Representations, Abstractions, and Priors for Robot Learning (RAP4Robots) @ ICRA 2023, May 2023 (Published) arXiv Poster BibTeX

Empirical Inference Ph.D. Thesis Learning with and for discrete optimization Paulus, M. ETH Zurich, Switzerland, May 2023, CLS PhD Program (Published) BibTeX

Perceiving Systems Empirical Inference Conference Paper MeshDiffusion: Score-based Generative 3D Mesh Modeling Liu, Z., Feng, Y., Black, M. J., Nowrouzezahrai, D., Paull, L., Liu, W. The Eleventh International Conference on Learning Representations (ICLR), ICLR, May 2023 (Published)

Abstract ›

We consider the task of generating realistic 3D shapes, which is useful for a variety of applications such as automatic scene generation and physical simulation. Compared to other 3D representations like voxels and point clouds, meshes are more desirable in practice, because (1) they enable easy and arbitrary manipulation of shapes for relighting and simulation, and (2) they can fully leverage the power of modern graphics pipelines which are mostly optimized for meshes. Previous scalable methods for generating meshes typically rely on sub-optimal post-processing, and they tend to produce overly-smooth or noisy surfaces without fine-grained geometric details. To overcome these shortcomings, we take advantage of the graph structure of meshes and use a simple yet very effective generative modeling method to generate 3D meshes. Specifically, we represent meshes with deformable tetrahedral grids, and then train a diffusion model on this direct parametrization. We demonstrate the effectiveness of our model on multiple generative tasks.

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Publications

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