Publications

Empirical Inference Master Thesis Denoising Representation Learning for Causal Discovery Sakenyte, U. Université de Genèva, Switzerland, December 2023, external supervision (Published) BibTeX

Empirical Inference Conference Paper Flow Matching for Scalable Simulation-Based Inference Wildberger*, J., Dax*, M., Buchholz*, S., Green, S. R., Macke, J. H., Schölkopf, B. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:16837-16864, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023, *equal contribution (Published) URL BibTeX

Empirical Inference Conference Paper Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation Gao*, R., Deistler*, M., Macke, J. H. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:80191-80219, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023, *equal contribution (Published) URL BibTeX

Empirical Inference Conference Paper Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures Eschenhagen, R., Immer, A., Turner, R., Schneider, F., Hennig, P. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:33624-33655, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023 (Published) URL BibTeX

Empirical Inference Conference Paper Learning Endogenous Representation in Reinforcement Learning via Advantage Estimation Pan, H., Schölkopf, B. Causal Representation Learning Workshop at NeurIPS 2023, December 2023 (Published) URL BibTeX

Empirical Inference Conference Paper Learning Layer-wise Equivariances Automatically using Gradients van der Ouderaa, T., Immer, A., van der Wilk, M. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:28365-28377, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023 (Published) URL BibTeX

Empirical Inference Conference Paper Learning Linear Causal Representations from Interventions under General Nonlinear Mixing Buchholz*, S., Rajendran*, G., Rosenfeld, E., Aragam, B., Schölkopf, B., Ravikumar, P. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:45419-45462, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023, *equal contribution (Published) URL BibTeX

Empirical Inference Conference Paper Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference Confavreux*, B., Ramesh*, P., Goncalves, P. J., Macke, J. H., Vogels, T. P. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:13545-13558, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023, *equal contribution (Published) URL BibTeX

Empirical Inference Article Multimodal learning in clinical proteomics: enhancing antimicrobial resistance prediction models with chemical information Visonà, G., Duroux, D., Miranda, L., Sükei, E., Li, Y., Borgwardt, K., Oliver, C. Bioinformatics, 39(12), December 2023 (Published) DOI BibTeX

Empirical Inference Conference Paper Neural Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning Munkhoeva, M., Oseledets, I. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:60712-60723, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023 (Published) URL BibTeX

Empirical Inference Conference Paper Nonparametric Identifiability of Causal Representations from Unknown Interventions von Kügelgen, J., Besserve, M., Liang, W., Gresele, L., Kekić, A., Bareinboim, E., Blei, D., Schölkopf, B. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:48603-48638, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023 (Published) URL BibTeX

Empirical Inference Conference Paper Nonparametric Teaching for Multiple Learners Zhang, C., Cao, X., Liu, W., Tsang, I. W., Kwok, J. T. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:7756-7786, (Editors: A. Oh and T. Naumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023 (Published) URL BibTeX

Empirical Inference Conference Paper On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series Kuznetsova*, R., Pace*, A., Burger*, M., Yèche, H., Rätsch, G. Proceedings of the 3rd Machine Learning for Health Symposium (ML4H) , 225:268-291, Proceedings of Machine Learning Research, (Editors: Hegselmann, S.and Parziale, A. and Shanmugam, D. and Tang, S. and Asiedu, M. N. and Chang, S. and Hartvigsen, T. and Singh, H.), PMLR, December 2023, *equal contribution (Published) URL BibTeX

Empirical Inference Conference Paper SE(3) Equivariant Augmented Coupling Flows Midgley*, L. I., Stimper*, V., Antorán*, J., Mathieu*, E., Schölkopf, B., Hernández-Lobato, J. M. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:79200-79225, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023, *equal contribution (Published)

Abstract ›

Coupling normalizing flows allow for fast sampling and density evaluation, making them the tool of choice for probabilistic modeling of physical systems. However, the standard coupling architecture precludes endowing flows that operate on the Cartesian coordinates of atoms with the SE(3) and permutation invariances of physical systems. This work proposes a coupling flow that preserves SE(3) and permutation equivariance by performing coordinate splits along additional augmented dimensions. At each layer, the flow maps atoms’ positions into learned SE(3) invariant bases, where we apply standard flow transformations, such as monotonic rational-quadratic splines, before returning to the original basis. Crucially, our flow preserves fast sampling and density evaluation, and may be used to produce unbiased estimates of expectations with respect to the target distribution via importance sampling. When trained on the DW4, LJ13 and QM9-positional datasets, our flow is competitive with equivariant continuous normalizing flows, while allowing sampling two orders of magnitude faster. Moreover, to the best of our knowledge, we are the first to learn the full Boltzmann distribution of alanine dipeptide by only modeling the Cartesian positions of its atoms. Lastly, we demonstrate that our flow can be trained to approximately sample from the Boltzmann distribution of the DW4 and LJ13 particle systems using only their energy functions.

arXiv URL BibTeX

Empirical Inference Conference Paper Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent Lin*, J. A., Antorán*, J., Padhy*, S., Janz, D., Hernández-Lobato, J. M., Terenin, A. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:36886-36912, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023, *equal contribution (Published) URL BibTeX

Empirical Inference Conference Paper Spuriosity Didn’t Kill the Classifier: Using Invariant Predictions to Harness Spurious Features Eastwood*, C., Singh*, S., Nicolicioiu, A. L., Vlastelica, M., von Kügelgen, J., Schölkopf, B. In Advances in Neural Information Processing Systems 36, 36:18291-18324, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023, *equal contribution (Published) URL BibTeX

Empirical Inference Conference Paper Spuriosity Didn’t Kill the Classifier: Using Invariant Predictions to Harness Spurious Features Eastwood*, C., Singh*, S., Nicolicioiu, A. L., Vlastelica, M., von Kügelgen, J., Schölkopf, B. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:18291-18324, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023, *equal contribution (Published) URL BibTeX

Empirical Inference Ph.D. Thesis Stochastic Predictive Control for Legged Robots Gazar, A. University of Tübingen, Germany, December 2023 (Published) DOI BibTeX

Empirical Inference Learning and Dynamical Systems IT Services Robotics Conference Paper A Robust Open-source Tendon-driven Robot Arm for Learning Control of Dynamic Motions Guist, S., Schneider, J., Ma, H., Berenz, V., Martus, J., Grüninger, F., Muehlebach, M., Fiene, J., Schölkopf, B., Büchler, B. RoboLetics: Workshop on Robot Learning in Athletics @CoRL 2023, November 2023 (Published) URL BibTeX

Empirical Inference Article Data-Efficient Learning via Minimizing Hyperspherical Energy Cao, X., Liu, W., Tsang, I. W. IEEE transactions on pattern analysis and machine intelligence, 45(11):13422-13437, November 2023 (Published) DOI BibTeX

Empirical Inference Article Deep learning-based age estimation from clinical Computed Tomography image data of the thorax and abdomen in the adult population Kerber, B., Hepp, T., Küstner, T., Gatidis, S. PLOS ONE, 18(11), November 2023 (Published) DOI BibTeX

Empirical Inference Article Kernel-Based Independence Tests for Causal Structure Learning on Functional Data Laumann, F., von Kügelgen, J., Park, J., Schölkopf, B., Barahona, M. Entropy, 25(12), November 2023 (Published) DOI BibTeX

Empirical Inference Bachelor Thesis Navigating the Ocean of Biases: Political Bias Attribution in Language Models via Causal Structures Jenny, D. ETH Zurich, Switzerland, November 2023, external supervision (Published) BibTeX

Empirical Inference Article Variational Causal Dynamics: Discovering Modular World Models from Interventions Lei, A., Schölkopf, B., Posner, I. Transactions on Machine Learning Research, November 2023 (Published) URL BibTeX

Empirical Inference Article A taxonomy and review of generalization research in NLP Hupkes, D., Giulianelli, M., Dankers, V., Artetxe, M., Elazar, Y., Pimentel, T., Christodoulopoulos, C., Lasri, K., Saphra, N., Sinclair, A., Ulmer, D., Schottmann, F., Batsuren, K., Sun, K., Sinha, K., Khalatbari, L., Ryskina, M., Frieske, R., Cotterell, R., Jin, Z. Nature Machine Intelligence, 5(10):1161-1174, October 2023 (Published) DOI BibTeX

Empirical Inference Article Artificial Intelligence in Oncological Hybrid Imaging Feuerecker, B., Heimer, M. M., Geyer, T., Fabritius, M. P., Gu, S., Schachtner, B., Beyer, L., Ricke, J., Gatidis, S., Ingrisch, M., Cyran, C. C. Nuklearmedizin, 62(5):296-305, October 2023 (Published) DOI BibTeX

Empirical Inference Article Can Whole-Body Baseline CT Radiomics Add Information to the Prediction of Best Response, Progression-Free Survival, and Overall Survival of Stage IV Melanoma Patients Receiving First-Line Targeted Therapy: A Retrospective Register Study Peisen, F., Gerken, A., Dahm, I., Nikolaou, K., Eigentler, T., Amaral, T., Moltz, J. H., Othman, A. E., Gatidis, S., Dondi, F. Diagnostics (Basel), 13(20), October 2023 (Published) DOI BibTeX

Empirical Inference Perceiving Systems Conference Paper One-shot Implicit Animatable Avatars with Model-based Priors Huang, Y., Yi, H., Liu, W., Wang, H., Wu, B., Wang, W., Lin, B., Zhang, D., Cai, D. In Proc. International Conference on Computer Vision (ICCV), 8940-8951, International Conference on Computer Vision, October 2023, *equal contribution (Published)

Abstract ›

Existing neural rendering methods for creating human avatars typically either require dense input signals such as video or multi-view images, or leverage a learned prior from large-scale specific 3D human datasets such that reconstruction can be performed with sparse-view inputs. Most of these methods fail to achieve realistic reconstruction when only a single image is available. To enable the data-efficient creation of realistic animatable 3D humans, we propose ELICIT, a novel method for learning human-specific neural radiance fields from a single image. Inspired by the fact that humans can easily reconstruct the body geometry and infer the full-body clothing from a single image, we leverage two priors in ELICIT: 3D geometry prior and visual semantic prior. Specifically, ELICIT introduces the 3D body shape geometry prior from a skinned vertex-based template model (i.e., SMPL) and implements the visual clothing semantic prior with the CLIP-based pre-trained models. Both priors are used to jointly guide the optimization for creating plausible content in the invisible areas. In order to further improve visual details, we propose a segmentation-based sampling strategy that locally refines different parts of the avatar.Comprehensive evaluations on multiple popular benchmarks, including ZJU-MoCAP, Human3.6M, and DeepFashion, show that ELICIT has outperformed current state-of-the-art avatar creation methods when only a single image is available. Code will be public for reseach purpose at https://github.com/huangyangyi/ELICIT

arXiv code project DOI BibTeX

Perceiving Systems Empirical Inference Conference Paper Pairwise Similarity Learning is SimPLE Wen, Y., Liu, W., Feng, Y., Raj, B., Singh, R., Weller, A., Black, M. J., Schölkopf, B. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), International Conference on Computer Vision, October 2023 (Published)

Abstract ›

In this paper, we focus on a general yet important learning problem, pairwise similarity learning (PSL). PSL subsumes a wide range of important applications, such as open-set face recognition, speaker verification, image retrieval and person re-identification. The goal of PSL is to learn a pairwise similarity function assigning a higher similarity score to positive pairs (i.e., a pair of samples with the same label) than to negative pairs (i.e., a pair of samples with different label). We start by identifying a key desideratum for PSL, and then discuss how existing methods can achieve this desideratum. We then propose a surprisingly simple proxy-free method, called SimPLE, which requires neither feature/proxy normalization nor angular margin and yet is able to generalize well in open-set recognition. We apply the proposed method to three challenging PSL tasks: open-set face recognition, image retrieval and speaker verification. Comprehensive experimental results on large-scale benchmarks show that our method performs significantly better than current state-of-the-art methods.

URL BibTeX

Empirical Inference Article CROCODILE - Incorporating medium-resolution spectroscopy of close-in directly imaged exoplanets into atmospheric retrievals via cross-correlation Hayoz, J., Cugno, G., Quanz, S. P., Patapis, P., Alei, E., Bonse, M. J., Dannert, F. A., Garvin, E. O., Gebhard, T. D., Konrad, B. S., Sartori, L. F. Astronomy & Astrophysics, 678, October 2023 (Published) DOI BibTeX

Empirical Inference Article A historical perspective of biomedical explainable AI research Malinverno, L., Barros, V., Ghisoni, F., Visonà, G., Kern, R., Nickel, P. J., Ventura, B. E., Šimić, I., Stryeck, S., Manni, F., Ferri, C., Jean-Quartier, C., Genga, L., Schweikert, G., Lovrić, M., Rosen-Zvi, M. Patterns, 4(9), September 2023 (Published) DOI BibTeX

Empirical Inference Conference Paper Certified private data release for sparse Lipschitz functions Donhauser, K., Lokna, J., Sanyal, A., Boedihardjo, M., Hönig, R., Yang, F. TPDP 2023 - Theory and Practice of Differential Privacy, September 2023 (Published) arXiv URL BibTeX

Empirical Inference Master Thesis Efficient Sampling from Differentiable Matrix Elements Kofler, A. Technical University of Munich, Germany, September 2023 (Published) BibTeX

Empirical Inference Conference Paper How to make semi-private learning more effective Pinto, F., Hu, Y., Yang, F., Sanyal, A. TPDP 2023 - Theory and Practice of Differential Privacy, September 2023 (Published) arXiv URL BibTeX

Empirical Inference Article Neural Causal Structure Discovery from Interventions Ke*, N. R., Bilaniuk*, O., Goyal, A., Bauer, S., Larochelle, H., Schölkopf, B., Mozer, M. C., Pal, C., Bengio, Y. Transactions on Machine Learning Research, September 2023, *equal contribution (Published) URL BibTeX

Empirical Inference Article Simulation-based inference for efficient identification of generative models in computational connectomics Boelts, J., Harth, P., Gao, R., Udvary, D., Yáñez, F., Baum, D., Hege, H., Oberlaender, M., Macke, J. H. PLOS Computational Biology, 19(9):1-28, September 2023 (Published) DOI BibTeX

Empirical Inference Article Some Remarks on Identifiability of Independent Component Analysis in Restricted Function Classes Buchholz, S. Transactions on Machine Learning Research, September 2023 (Published) URL BibTeX

Learning and Dynamical Systems Empirical Inference Conference Paper Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimators Kladny, K., von Kügelgen, J., Schölkopf, B., Muehlebach, M. Conference on Uncertainty in Artificial Intelligence, 216:1087-1097, Proceedings of Machine Learning Research, (Editors: Evans, Robin J. and Shpitser, Ilya), PMLR, August 2023 (Published) URL BibTeX

Empirical Inference Article Chasing rainbows and ocean glints: Inner working angle constraints for the Habitable Worlds Observatory Vaughan, S. R., Gebhard, T. D., Bott, K., Casewell, S. L., Cowan, N. B., Doelman, D. S., Kenworthy, M., Mazoyer, J., Millar-Blanchaer, M. A., Trees, V. J. H., Stam, D. M., Absil, O., Altinier, L., Baudoz, P., Belikov, R., Bidot, A., Birkby, J. L., Bonse, M. J., Brandl, B., Carlotti, A., et al. Monthly Notices of the Royal Astronomical Society, 524(4):5477-5485, August 2023 (Published) DOI BibTeX

Empirical Inference Article Getting personal with epigenetics: towards individual-specific epigenomic imputation with machine learning Hawkins-Hooker, A., Visonà, G., Narendra, T., Rojas-Carulla, M., Schölkopf, B., Schweikert, G. Nature Communications, 14(1), August 2023 (Published) DOI BibTeX

Empirical Inference Ph.D. Thesis Less is more: Discovering Redundancies in Data and Neural Network Models for Improved Efficiency, Interpretability and Privacy Adamczewski, K. ETH Zurich, Switzerland, August 2023 (Published) BibTeX

Haptic Intelligence Autonomous Learning Empirical Inference Article Minsight: A Fingertip-Sized Vision-Based Tactile Sensor for Robotic Manipulation Andrussow, I., Sun, H., Kuchenbecker, K. J., Martius, G. Advanced Intelligent Systems, 5(8):2300042, August 2023, Inside back cover, DOI: 10.1002/aisy.202370035 (Published)

Abstract ›

Intelligent interaction with the physical world requires perceptual abilities beyond vision and hearing; vibrant tactile sensing is essential for autonomous robots to dexterously manipulate unfamiliar objects or safely contact humans. Therefore, robotic manipulators need high-resolution touch sensors that are compact, robust, inexpensive, and efficient. The soft vision-based haptic sensor presented herein is a miniaturized and optimized version of the previously published sensor Insight. Minsight has the size and shape of a human fingertip and uses machine learning methods to output high-resolution maps of 3D contact force vectors at 60 Hz. Experiments confirm its excellent sensing performance, with a mean absolute force error of 0.07 N and contact location error of 0.6 mm across its surface area. Minsight's utility is shown in two robotic tasks on a 3-DoF manipulator. First, closed-loop force control enables the robot to track the movements of a human finger based only on tactile data. Second, the informative value of the sensor output is shown by detecting whether a hard lump is embedded within a soft elastomer with an accuracy of 98\%. These findings indicate that Minsight can give robots the detailed fingertip touch sensing needed for dexterous manipulation and physical human–robot interaction.

DOI BibTeX

Empirical Inference Conference Paper Socially Responsible Machine Learning: A Causal Perspective Moraffah, R., Karimi, A., Raglin, A., Liu, H. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 5819-5820, Association for Computing Machinery, August 2023 (Published) DOI BibTeX

Empirical Inference Conference Paper USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution Rangnekar, V., Upadhyay, U., Akata, Z., Banerjee, B. Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI), 216:1707-1717, Proceedings of Machine Learning Research, (Editors: Evans, Robin J. and Shpitser, Ilya), PMLR, August 2023 (Published) URL BibTeX

Empirical Inference Conference Paper A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models Stolfo, A., Jin, Z., Shridhar, K., Schölkopf, B., Sachan, M. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), Volume 1: Long Papers:545-561, (Editors: Rogers, A. and Boyd-Graber, J. L. and Okazaki, N.), Association for Computational Linguistics, July 2023 (Published) DOI BibTeX

Empirical Inference Article A network approach to atomic spectra Wellnitz, D., Kekić, A., Heiss, J., Gertz, M., Weidemüller, M., Spitz, A. Journal of Physics: Complexity, 4(3), July 2023 (Published) DOI BibTeX

Empirical Inference Ph.D. Thesis Advances in Algorithmic Recourse: Ensuring Causal Consistency, Fairness, & Robustness Karimi, A. ETH Zurich, Switzerland, July 2023 (Published) BibTeX

Empirical Inference Conference Paper Adversarial robustness of amortized Bayesian inference Glöckler, M., Deistler, M., Macke, J. H. Proceedings of 40th International Conference on Machine Learning (ICML) , 202:11493-11524, 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 Article Catastrophic overfitting can be induced with discriminative non-robust features Ortiz-Jimenez*, G., de Jorge*, P., Sanyal, A., Bibi, A., Dokania, P. K., Frossard, P., Rogez, G., Torr, P. Transactions on Machine Learning Research , July 2023, *equal contribution (Published) PDF Code URL BibTeX

Empirical Inference Conference Paper Certifying Ensembles: A General Certification Theory with S-Lipschitzness Petrov, A., Eiras, F., Sanyal, A., Torr, P., Bibi, A. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:27709-27736, 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

Publications

Filter by