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87 Publications
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2022 | Published | Conference Paper | IST-REx-ID: 14174 | 
Dittadi A, Träuble F, Wüthrich M, Widmaier F, Gehler P, Winther O, Locatello F, Bachem O, Schölkopf B, Bauer S. 2022. The role of pretrained representations for the OOD generalization of reinforcement learning agents. 10th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.
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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 14175 |
Makansi O, Kügelgen J von, Locatello F, Gehler P, Janzing D, Brox T, Schölkopf B. 2022. You mostly walk alone: Analyzing feature attribution in trajectory prediction. 10th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.
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| arXiv
2022 | Submitted | Conference Paper | IST-REx-ID: 14215 |
Rahaman N, Weiss M, Träuble F, Locatello F, Lacoste A, Bengio Y, Pal C, Li LE, Schölkopf B. A general purpose neural architecture for geospatial systems. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems.
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| arXiv
2022 | Submitted | Preprint | IST-REx-ID: 14220 |
Mambelli D, Träuble F, Bauer S, Schölkopf B, Locatello F. Compositional multi-object reinforcement learning with linear relation networks. arXiv, 2201.13388.
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| arXiv
2021 | Published | Journal Article | IST-REx-ID: 14117 |
Scholkopf B, Locatello F, Bauer S, Ke NR, Kalchbrenner N, Goyal A, Bengio Y. 2021. Toward causal representation learning. Proceedings of the IEEE. 109(5), 612–634.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 14176 |
Yèche H, Dresdner G, Locatello F, Hüser M, Rätsch G. 2021. Neighborhood contrastive learning applied to online patient monitoring. Proceedings of 38th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 139, 11964–11974.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 14177 |
Träuble F, Creager E, Kilbertus N, Locatello F, Dittadi A, Goyal A, Schölkopf B, Bauer S. 2021. On disentangled representations learned from correlated data. Proceedings of the 38th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 139, 10401–10412.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 14178 |
Dittadi A, Träuble F, Locatello F, Wüthrich M, Agrawal V, Winther O, Bauer S, Schölkopf B. 2021. On the transfer of disentangled representations in realistic settings. The Ninth International Conference on Learning Representations. ICLR: International Conference on Learning Representations.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 14179 |
Kügelgen J von, Sharma Y, Gresele L, Brendel W, Schölkopf B, Besserve M, Locatello F. 2021. Self-supervised learning with data augmentations provably isolates content from style. Advances in Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 16451–16467.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 14180 |
Rahaman N, Gondal MW, Joshi S, Gehler P, Bengio Y, Locatello F, Schölkopf B. 2021. Dynamic inference with neural interpreters. Advances in Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 10985–10998.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 14181 |
Dresdner G, Shekhar S, Pedregosa F, Locatello F, Rätsch G. 2021. Boosting variational inference with locally adaptive step-sizes. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. IJCAI: International Joint Conference on Artificial Intelligence, 2337–2343.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 14182 |
Träuble F, Kügelgen J von, Kleindessner M, Locatello F, Schölkopf B, Gehler P. 2021. Backward-compatible prediction updates: A probabilistic approach. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 116–128.
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| arXiv
2021 | Submitted | Preprint | IST-REx-ID: 14221 |
Locatello F. Enforcing and discovering structure in machine learning. arXiv, 2111.13693.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 14332
Träuble F, Dittadi A, Wuthrich M, Widmaier F, Gehler PV, Winther O, Locatello F, Bachem O, Schölkopf B, Bauer S. 2021. Representation learning for out-of-distribution generalization in reinforcement learning. ICML 2021 Workshop on Unsupervised Reinforcement Learning. ICML: International Conference on Machine Learning.
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2021 | Patent | IST-REx-ID: 14185 |
Weissenborn D, Uszkoreit J, Unterthiner T, Mahendran A, Locatello F, Kipf T, Heigold G, Dosovitskiy A. 2021. Object-centric learning with slot attention.
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| arXiv
2020 | Published | Journal Article | IST-REx-ID: 14125 |
Stark SG et al. 2020. SCIM: Universal single-cell matching with unpaired feature sets. Bioinformatics. 36(Supplement_2), i919–i927.
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| PubMed | Europe PMC
2020 | Published | Conference Paper | IST-REx-ID: 14186 |
Locatello F, Bauer S, Lucic M, Rätsch G, Gelly S, Schölkopf B, Bachem O. 2020. A commentary on the unsupervised learning of disentangled representations. The 34th AAAI Conference on Artificial Intelligence. AAAI: Conference on Artificial Intelligence vol. 34, 13681–13684.
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| arXiv
2020 | Published | Conference Paper | IST-REx-ID: 14187 |
Négiar G, Dresdner G, Tsai A, Ghaoui LE, Locatello F, Freund RM, Pedregosa F. 2020. Stochastic Frank-Wolfe for constrained finite-sum minimization. Proceedings of the 37th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 119, 7253–7262.
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| arXiv
2020 | Published | Conference Paper | IST-REx-ID: 14188 |
Locatello F, Poole B, Rätsch G, Schölkopf B, Bachem O, Tschannen M. 2020. Weakly-supervised disentanglement without compromises. Proceedings of the 37th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 119, 6348–6359.
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| arXiv
2020 | Published | Journal Article | IST-REx-ID: 14195 |
Locatello F, Bauer S, Lucic M, Rätsch G, Gelly S, Schölkopf B, Bachem O. 2020. A sober look at the unsupervised learning of disentangled representations and their evaluation. Journal of Machine Learning Research. 21, 209.
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| arXiv
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