Robust Machine Learning Article Interaction Asymmetry: A General Principle for Learning Composable Abstractions Brady, J., von Kügelgen, J., Lachapelle, S., Buchholz, S., Kipf, T., Brendel, W. November 2024 (Submitted) BibTeX

Learning and Dynamical Systems Empirical Inference Article Deep Backtracking Counterfactuals for Causally Compliant Explanations Kladny, K., Kügelgen, J. V., Schölkopf, B., Muehlebach, M. Transactions on Machine Learning Research, July 2024 (Published) arXiv URL BibTeX

Empirical Inference Autonomous Learning Conference Paper Multi-View Causal Representation Learning with Partial Observability Yao, D., Xu, D., Lachapelle, S., Magliacane, S., Taslakian, P., Martius, G., von Kügelgen, J., Locatello, F. The Twelfth International Conference on Learning Representations (ICLR), May 2024 (Published) arXiv BibTeX

Empirical Inference Ph.D. Thesis Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment von Kügelgen, J. University of Cambridge, UK, Cambridge, February 2024, (Cambridge-Tübingen-Fellowship) (Published) URL BibTeX

Learning and Dynamical Systems Article Backtracking Counterfactuals for Deep Structural Causal Models Kladny, K., von Kügelgen, J., Schölkopf, B., Muehlebach, M. Causal Inference Workshop, Conference on Uncertainty in Artificial Intelligence, 2024 (Published) BibTeX

Empirical Inference Conference Paper Causal Component Analysis Liang, W., Kekić, A., von Kügelgen, J., Buchholz, S., Besserve, M., Gresele*, L., Schölkopf*, B. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:32481-32520, (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, *shared last author (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 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 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

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