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Empirical Inference Members Publications

Members

Empirical Inference
Julius von Kügelgen
  • Doctoral Researcher
Empirical Inference
Luigi Gresele
  • Doctoral Researcher
Empirical Inference
Michel Besserve
Senior Research Scientist
Empirical Inference
Felix Leeb
  • Doctoral Researcher
Empirical Inference
Bernhard Schölkopf
  • Director
Empirical Inference
Francesco Locatello
Empirical Inference
Frederik Träuble
  • Doctoral Researcher
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Empirical Inference
Anirudh Goyal
Empirical Inference
Giambattista Parascandolo
  • Doctoral Researcher
Empirical Inference
Paul Rubenstein
  • Doctoral Researcher
Empirical Inference
Dominik Janzing
  • Research Scientist
Empirical Inference
Sebastian Weichwald
PhD Student
Empirical Inference
Alexander Neitz
Empirical Inference
Arash Mehrjou
Collaborator

Publications

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 Conference Paper Independent mechanisms analysis, a new concept? Gresele*, L., von Kügelgen*, J., Stimper, V., Schölkopf, B., Besserve, M. Advances in Neural Information Processing Systems 34 (NeurIPS 2021), 28233-28248, (Editors: M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan), Curran Associates, Inc., Red Hook, NY, 35th Annual Conference on Neural Information Processing Systems, December 2021, *equal contribution (Published) arXiv URL BibTeX
Empirical Inference Conference Paper Self-supervised learning with data augmentations provably isolates content from style von Kügelgen*, J., Sharma*, Y., Gresele*, L., Brendel, W., Schölkopf, B., Besserve, M., Locatello, F. Advances in Neural Information Processing Systems 34 (NeurIPS 2021), 16451-16467, (Editors: M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan), Curran Associates, Inc., 35th Annual Conference on Neural Information Processing Systems, December 2021, *equal contribution (Published) arXiv URL BibTeX
Empirical Inference Conference Paper Function Contrastive Learning of Transferable Meta-Representations Gondal, M. W., Joshi, S., Rahaman, N., Bauer, S., Wüthrich, M., Schölkopf, B. Proceedings of 38th International Conference on Machine Learning (ICML), 139:3755-3765, Proceedings of Machine Learning Research, (Editors: Meila, Marina and Zhang, Tong), PMLR, July 2021 (Published) URL BibTeX
Empirical Inference Conference Paper On Disentangled Representations Learned From Correlated Data Träuble, F., Creager, E., Kilbertus, N., Locatello, F., Dittadi, A., Goyal, A., Schölkopf, B., Bauer, S. Proceedings of 38th International Conference on Machine Learning (ICML), 139:10401-10412, Proceedings of Machine Learning Research, (Editors: Meila, Marina and Zhang, Tong), PMLR, July 2021 (Published) URL BibTeX
Empirical Inference Conference Paper Learning explanations that are hard to vary Parascandolo*, G., Neitz*, A., Orvieto, A., Gresele, L., Schölkopf, B. In 9th International Conference on Learning Representations (ICLR), May 2021, *equal contribution (Published) arXiv URL BibTeX
Empirical Inference Conference Paper Recurrent Independent Mechanisms Goyal, A., Lamb, A., Hoffmann, J., Sodhani, S., Levine, S., Bengio, Y., Schölkopf, B. In The Ninth International Conference on Learning Representations (ICLR), 9th International Conference on Learning Representations (ICLR 2021), May 2021 (Published) URL BibTeX
Empirical Inference Conference Paper A Theory of Independent Mechanisms for Extrapolation in Generative Models Besserve, M., Sun, R., Janzing, D., Schölkopf, B. In Proceedings of the 35th AAAI Conference on Artificial Intelligence , 35(8):6741-6749, 35th AAAI Conference on Artificial Intelligence (AAAI 2021), February 2021 (Published) arXiv DOI URL BibTeX
Empirical Inference Article Toward Causal Representation Learning Schölkopf*, B., Locatello*, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., Bengio, Y. Proceedings of the IEEE, 109(5):612-634, 2021, *equal contribution (Published) DOI URL BibTeX
Empirical Inference Conference Paper 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 (NeurIPS 2020), 11525-11538, (Editors: H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin), Curran Associates, Inc., 34th Annual Conference on Neural Information Processing Systems, December 2020 (Published) URL BibTeX
Empirical Inference Conference Paper Weakly-Supervised Disentanglement Without Compromises Locatello, F., Poole, B., Rätsch, G., Schölkopf, B., Bachem, O., Tschannen, M. Proceedings of the 37th International Conference on Machine Learning (ICML), 119:6348-6359, Proceedings of Machine Learning Research, (Editors: Hal Daumé III and Aarti Singh), PMLR, July 2020 (Published) URL BibTeX
Empirical Inference Conference Paper 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 (Published) URL BibTeX
Empirical Inference Conference Paper 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 (Published) arXiv URL BibTeX
Empirical Inference Conference Paper 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 (Published) arXiv PDF BibTeX
Empirical Inference Conference Paper The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA Gresele*, L., Rubenstein*, P. K., Mehrjou, A., Locatello, F., Schölkopf, B. Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), 115:217-227, Proceedings of Machine Learning Research, (Editors: Adams, Ryan P. and Gogate, Vibhav), PMLR, July 2019, *equal contribution (Published) URL BibTeX
Empirical Inference Conference Paper Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations Locatello, F., Bauer, S., Lucic, M., Raetsch, G., Gelly, S., Schölkopf, B., Bachem, O. Proceedings of the 36th International Conference on Machine Learning (ICML), 97:4114-4124, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (Published) PDF URL BibTeX
Empirical Inference Conference Paper Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models Neitz, A., Parascandolo, G., Bauer, S., Schölkopf, B. Advances in Neural Information Processing Systems 31 (NeurIPS 2018), 9838-9848, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32nd Annual Conference on Neural Information Processing Systems, December 2018 (Published) arXiv URL BibTeX
Empirical Inference Conference Paper From Deterministic ODEs to Dynamic Structural Causal Models Rubenstein, P. K., Bongers, S., Schölkopf, B., Mooij, J. M. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI), 114-123, (Editors: Globerson, Amir and Silva, Ricardo), August 2018 (Published) Arxiv URL BibTeX
Empirical Inference Conference Paper Learning Independent Causal Mechanisms Parascandolo, G., Kilbertus, N., Rojas-Carulla, M., Schölkopf, B. Proceedings of the 35th International Conference on Machine Learning (ICML), 80:4033-4041, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (Published) URL BibTeX
Empirical Inference Conference Paper Group invariance principles for causal generative models Besserve, M., Shajarisales, N., Schölkopf, B., Janzing, D. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 84:557-565, Proceedings of Machine Learning Research, (Editors: Amos Storkey and Fernando Perez-Cruz), PMLR, April 2018 (Published) URL BibTeX
Empirical Inference Conference Paper Causal Consistency of Structural Equation Models Rubenstein*, P. K., Weichwald*, S., Bongers, S., Mooij, J. M., Janzing, D., Grosse-Wentrup, M., Schölkopf, B. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), ID 11, (Editors: Gal Elidan, Kristian Kersting, and Alexander T. Ihler), August 2017, *equal contribution (Published) Arxiv PDF URL BibTeX
Empirical Inference Conference Paper Causal Discovery from Temporally Aggregated Time Series Gong, M., Zhang, K., Schölkopf, B., Glymour, C., Tao, D. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), ID 269, (Editors: Gal Elidan, Kristian Kersting, and Alexander T. Ihler), August 2017 (Published) URL BibTeX