Publications

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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Empirical Inference Conference Paper Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction Chen, W., Tripp, A., Hernández-Lobato, J. M. The Eleventh International Conference on Learning Representations (ICLR), May 2023 (Published) URL BibTeX

Empirical Inference Movement Generation and Control Conference Paper On the Use of Torque Measurement in Centroidal State Estimation Khorshidi, S., Gazar, A., Rotella, N., Naveau, M., Righetti, L., Bennewitz, M., Khadiv, M. IEEE International Conference on Robotics and Automation (ICRA), 9931-9937, May 2023 (Published) DOI BibTeX

Empirical Inference Article Staying and Returning dynamics of young children’s attention Kim, J., Singh, S., Vales, C., Keebler, E., Fisher, A. V., Thiessen, E. D. Developmental Science, 26(6), May 2023 (Published) DOI BibTeX

Empirical Inference Conference Paper Structure by Architecture: Structured Representations without Regularization Leeb, F., Lanzillotta, G., Annadani, Y., Besserve, M., Bauer, S., Schölkopf, B. The Eleventh International Conference on Learning Representations (ICLR), May 2023 (Published) URL BibTeX

Empirical Inference Conference Paper DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability Eastwood*, C., Nicolicioiu*, A. L., von Kügelgen*, J., Kekić, A., Träuble, F., Dittadi, A., Schölkopf, B. The Eleventh International Conference on Learning Representations (ICLR), May 2023, *equal contribution (Published) URL BibTeX

Empirical Inference Article Uncovering the Organization of Neural Circuits with Generalized Phase Locking Analysis Safavi, S., Panagiotaropoulos, T. I., Kapoor, V., Ramirez-Villegas, J. F., Logothetis, N., Besserve, M. PLOS Computational Biology, 19(4):45, Public Library of Science, April 2023 (Published) bioRxiv DOI BibTeX

Empirical Inference Article Adapting to noise distribution shifts in flow-based gravitational-wave inference Wildberger, J., Dax, M., Green, S. R., Gair, J., Pürrer, M., Macke, J. H., Buonanno, A., Schölkopf, B. Physical Review D, 107(8), April 2023 (Published) DOI BibTeX

Empirical Inference Conference Paper BaCaDI: Bayesian Causal Discovery with Unknown Interventions Hägele, A., Rothfuss, J., Lorch, L., Somnath, V. R., Schölkopf, B., Krause, A. Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS) , 206:1411-1436, Proceedings of Machine Learning Research, (Editors: Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem), PMLR, April 2023 (Published) URL BibTeX

Empirical Inference Conference Paper Backtracking Counterfactuals von Kügelgen, J., Mohamed, A., Beckers, S. Proceedings of the Second Conference on Causal Learning and Reasoning (CLeaR), 213:177-196, Proceedings of Machine Learning Research, (Editors: van der Schaar, Mihaela and Zhang, Cheng and Janzing, Dominik), PMLR, April 2023 (Published) URL BibTeX

Empirical Inference Conference Paper Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning Liu, Y., Alahi, A., Russell, C., Horn, M., Zietlow, D., Schölkopf, B., Locatello, F. Proceedings of the Second Conference on Causal Learning and Reasoning (CLeaR), 213:553-573, Proceedings of Machine Learning Research, (Editors: van der Schaar, Mihaela and Zhang, Cheng and Janzing, Dominik), PMLR, April 2023 (Published) URL BibTeX

Empirical Inference Conference Paper Dataflow graphs as complete causal graphs Paleyes, A., Guo, S., Schölkopf, B., Lawrence, N. D. 2nd International Conference on AI Engineering - Software Engineering for AI (CAIN), 7-12, IEEE, April 2023 (Published) arXiv DOI BibTeX

Empirical Inference Article Instrumental variable regression via kernel maximum moment loss Zhang, R., Imaizumi, M., Schölkopf, B., Muandet, K. Journal of Causal Inference, 11(1), April 2023 (Published) DOI BibTeX

Empirical Inference Conference Paper Iterative Teaching by Data Hallucination Qiu, Z., Liu, W., Xiao, T., Liu, Z., Bhatt, U., Luo, Y., Weller, A., Schölkopf, B. Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS) , 206:9892-9913, Proceedings of Machine Learning Research, (Editors: Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem), PMLR, April 2023 (Published) URL BibTeX

Empirical Inference Robust Machine Learning Article Jacobian-based Causal Discovery with Nonlinear ICA Reizinger, P., Sharma, Y., Bethge, M., Schölkopf, B., Huszár, F., Brendel, W. Transactions on Machine Learning Research, April 2023 (Published) URL BibTeX

Empirical Inference Article Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference Dax, M., Green, S. R., Gair, J., Pürrer, M., Wildberger, J., Macke, J. H., Buonanno, A., Schölkopf, B. Physical Review Letters, 130(17), April 2023 (Published) DOI BibTeX

Empirical Inference Conference Paper Nonparametric Indirect Active Learning Singh, S. Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS) , 206:2515-2541, Proceedings of Machine Learning Research, (Editors: Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem), PMLR, April 2023 (Published) URL BibTeX

Empirical Inference Conference Paper On the Interventional Kullback-Leibler Divergence Wildberger, J. B., Guo, S., Bhattacharyya, A., Schölkopf, B. Proceedings of the Second Conference on Causal Learning and Reasoning (CLeaR), 213:328-349, Proceedings of Machine Learning Research, (Editors: van der Schaar, Mihaela and Zhang, Cheng and Janzing, Dominik), PMLR, April 2023 (Published) URL BibTeX

Empirical Inference Article The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles Schreiber*, J., Boix*, C., Lee, J. W., Li, H., Guan, Y., Chang, C., Chang, J., Hawkins-Hooker, A., Schölkopf, B., Schweikert, G., Carulla, M. R., Canakoglu, A., Guzzo, F., Nanni, L., Masseroli, M., Carman, M. J., Pinoli, P., Hong, C., Yip, K. Y., Spence, J. P., et al. Genome Biology, 24, April 2023, *co‑first authors (Published) DOI BibTeX

Empirical Inference Conference Paper Unsupervised Object Learning via Common Fate Tangemann, M., Schneider, S., von Kügelgen, J., Locatello, F., Gehler, P., Brox, T., Kümmerer, M., Bethge, M., Schölkopf, B. Proceedings of the Second Conference on Causal Learning and Reasoning (CLeaR), 213:281-327, Proceedings of Machine Learning Research, (Editors: van der Schaar, Mihaela and Zhang, Cheng and Janzing, Dominik), PMLR, April 2023 (Published) arXiv URL BibTeX

Empirical Inference Master Thesis Intrinsic complexity and mechanisms of expressivity of cortical neurons Spieler, A. M. University of Tübingen, Germany, March 2023 (Published) BibTeX

Empirical Inference Optics and Sensing Laboratory Micro, Nano, and Molecular Systems Article Multiplane Diffractive Acoustic Networks Athanassiadis, A. G., Schlieder, L., Melde, K., Volchkov, V., Schölkopf, B., Fischer, P. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 70(5):441-448, IEEE, March 2023 (Published) DOI BibTeX

Empirical Inference Article Proactive Contact Tracing Gupta, P., Maharaj, T., Weiss, M., Rahaman, N., Alsdurf, H., Minoyan, N., Harnois-Leblanc, S., Merckx, J., Williams, A., Schmidt, V., St-Charles, P., Patel, A., Zhang, Y., Buckeridge, D. L., Pal, C., Schölkopf, B., Bengio, Y. PLOS Digital Health, 2(3):1-19, March 2023 (Published) DOI BibTeX

Empirical Inference Article Self-supervised contrastive learning with random walks for medical image segmentation with limited annotations Fischer, M., Hepp, T., Gatidis, S., Yang, B. Computerized Medical Imaging and Graphics, 104, Elsevier, Amsterdam, March 2023 (Published) DOI BibTeX

Empirical Inference Article Compact holographic sound fields enable rapid one-step assembly of matter in 3D Melde, K., Kremer, H., Shi, M., Seneca, S., Frey, C., Platzman, I., Degel, C., Schmitt, D., Schölkopf, B., Fischer, P. Science Advances, 9(6), AAAS, Washington, DC, February 2023 (Published) DOI URL BibTeX

Empirical Inference Article Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets Choe, J., Oh, S. J., Chun, S., Lee, S., Akata, Z., Shim, H. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2):1732-1748, IEEE, New York, NY, February 2023 (Published) DOI URL BibTeX

Learning and Dynamical Systems Empirical Inference Master Thesis Causal Effect Estimation by Combining Observational and Interventional Data Kladny, K. ETH Zurich, Switzerland, February 2023 (Published) BibTeX

Empirical Inference Article GRASP: Scalable Graph Alignment by Spectral Corresponding Functions Hermanns, J., Skitsas, K., Tsitsulin, A., Munkhoeva, M., Kyster, A., Nielsen, S., Bronstein, A. M., Mottin, D., Karras, P. ACM Transactions on Knowledge Discovery from Data, 17(4), February 2023 (Published) DOI BibTeX

Empirical Inference Article SphereFace Revived: Unifying Hyperspherical Face Recognition Liu, W., Wen, Y., Raj, B., Singh, R., Weller, A. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2):2458-2474, February 2023 (Published) DOI BibTeX

Empirical Inference Conference Paper Towards Empirical Process Theory for Vector-Valued Functions: Metric Entropy of Smooth Function Classes Park, J., Muandet, K. Proceedings of the 34th International Conference on Algorithmic Learning Theory (ALT), 201:1216-1260, Proceedings of Machine Learning Research, (Editors: Agrawal, Shipra and Orabona, Francesco), PMLR, February 2023 (Published) URL BibTeX

Empirical Inference Master Thesis Towards Generative Machine Teaching Qui, Z. Technical University of Munich, Germany, February 2023 (Published) BibTeX

Empirical Inference Article ViViT: Curvature Access Through The Generalized Gauss-Newton’s Low-Rank Structure Dangel*, F., Tatzel*, L., Hennig, P. Transactions on Machine Learning Research, February 2023, *equal contribution (Published) URL BibTeX

Empirical Inference Article A machine learning route between band mapping and band structure Xian*, R. P., Stimper*, V., Zacharias, M., Dendzik, M., Dong, S., Beaulieu, S., Schölkopf, B., Wolf, M., Rettig, L., Carbogno, C., Bauer, S., Ernstorfer, R. Nature Computational Science, 3(1):101-114, January 2023, *equal contribution (Published) arXiv DOI BibTeX

Empirical Inference Master Thesis ArchiSound: Audio Generation with Diffusion Schneider, F. ETH Zurich, Switzerland, January 2023, external supervision (Published) BibTeX

Empirical Inference Article Audio Retrieval With Natural Language Queries: A Benchmark Study Koepke, A. S., Oncescu, A., Henriques, J. F., Akata, Z., Albanie, S. IEEE Transactions on Multimedia, 25:2675-2685, January 2023 (Published) DOI BibTeX

Empirical Inference Master Thesis Generation and Quantification of Spin in Robot Table Tennis Dittrich, A. University of Stuttgart, Germany, January 2023 (Published) BibTeX

Empirical Inference Article Learning Dynamical Systems using Local Stability Priors Mehrjou, A., Iannelli, A., Schölkopf, B. Journal of Computational Dynamics, 10(1):175-198, January 2023, Special issue "Computation of Lyapunov functions and contraction metrics" (Published) DOI BibTeX

Empirical Inference Article Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots Büchler, D., Calandra, R., Peters, J. Robotics and Autonomous Systems, 159, Elsevier, Amsterdam, January 2023 (Published)

Abstract ›

High-speed and high-acceleration movements are inherently hard to control. Applying learning to the control of such motions on anthropomorphic robot arms can improve the accuracy of the control but might damage the system. The inherent exploration of learning approaches can lead to instabilities and the robot reaching joint limits at high speeds. Having hardware that enables safe exploration of high-speed and high-acceleration movements is therefore desirable. To address this issue, we propose to use robots actuated by Pneumatic Artificial Muscles (PAMs). In this paper, we present a four degrees of freedom (DoFs) robot arm that reaches high joint angle accelerations of up to 28000 °/s^2 while avoiding dangerous joint limits thanks to the antagonistic actuation and limits on the air pressure ranges. With this robot arm, we are able to tune control parameters using Bayesian optimization directly on the hardware without additional safety considerations. The achieved tracking performance on a fast trajectory exceeds previous results on comparable PAM-driven robots. We also show that our system can be controlled well on slow trajectories with PID controllers due to careful construction considerations such as minimal bending of cables, lightweight kinematics and minimal contact between PAMs and PAMs with the links. Finally, we propose a novel technique to control the the co-contraction of antagonistic muscle pairs. Experimental results illustrate that choosing the optimal co-contraction level is vital to reach better tracking performance. Through the use of PAM-driven robots and learning, we do a small step towards the future development of robots capable of more human-like motions.

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Autonomous Learning Haptic Intelligence Empirical Inference Article Predicting the Force Map of an ERT-Based Tactile Sensor Using Simulation and Deep Networks Lee, H., Sun, H., Park, H., Serhat, G., Javot, B., Martius, G., Kuchenbecker, K. J. IEEE Transactions on Automation Science and Engineering, 20(1):425-439, January 2023 (Published)

Abstract ›

Electrical resistance tomography (ERT) can be used to create large-scale soft tactile sensors that are flexible and robust. Good performance requires a fast and accurate mapping from the sensor's sequential voltage measurements to the distribution of force across its surface. However, particularly with multiple contacts, this task is challenging for both previously developed approaches: physics-based modeling and end-to-end data-driven learning. Some promising results were recently achieved using sim-to-real transfer learning, but estimating multiple contact locations and accurate contact forces remains difficult because simulations tend to be less accurate with a high number of contact locations and/or high force. This paper introduces a modular hybrid method that combines simulation data synthesized from an electromechanical finite element model with real measurements collected from a new ERT-based tactile sensor. We use about 290,000 simulated and 90,000 real measurements to train two deep neural networks: the first (Transfer-Net) captures the inevitable gap between simulation and reality, and the second (Recon-Net) reconstructs contact forces from voltage measurements. The number of contacts, contact locations, force magnitudes, and contact diameters are evaluated for a manually collected multi-contact dataset of 150 measurements. Our modular pipeline's results outperform predictions by both a physics-based model and end-to-end learning.

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Empirical Inference Article Pyfectious: An individual-level simulator to discover optimal containment policies for epidemic diseases Mehrjou*, A., Soleymani*, A., Abyaneh, A., Bhatt, S., Schölkopf, B., Bauer, S. PLOS Computational Biology, 19(1):1-41, January 2023, *equal contribution (Published) DOI BibTeX

Empirical Inference Article Quantum machine learning beyond kernel methods Jerbi, S., Fiderer, L. J., Poulsen Nautrup, H., Kübler, J. M., Briegel, H. J., Dunjko, V. Nature Communications, 14(1), January 2023 (Published) DOI BibTeX

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