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71 Publications


2021 | Conference Paper | IST-REx-ID: 14176 | OA
Yèche, H., Dresdner, G., Locatello, F., Hüser, M., & Rätsch, G. (2021). Neighborhood contrastive learning applied to online patient monitoring. In Proceedings of 38th International Conference on Machine Learning (Vol. 139, pp. 11964–11974). Virtual: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2021 | Conference Paper | IST-REx-ID: 14182 | OA
Träuble, F., Kügelgen, J. von, Kleindessner, M., Locatello, F., Schölkopf, B., & Gehler, P. (2021). Backward-compatible prediction updates: A probabilistic approach. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 116–128). Virtual.
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2021 | Conference Paper | IST-REx-ID: 14181 | OA
Dresdner, G., Shekhar, S., Pedregosa, F., Locatello, F., & Rätsch, G. (2021). Boosting variational inference with locally adaptive step-sizes. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (pp. 2337–2343). Montreal, Canada: International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2021/322
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2021 | Conference Paper | IST-REx-ID: 14179 | OA
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. In Advances in Neural Information Processing Systems (Vol. 34, pp. 16451–16467). Virtual.
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2021 | Conference Paper | IST-REx-ID: 14180 | OA
Rahaman, N., Gondal, M. W., Joshi, S., Gehler, P., Bengio, Y., Locatello, F., & Schölkopf, B. (2021). Dynamic inference with neural interpreters. In Advances in Neural Information Processing Systems (Vol. 34, pp. 10985–10998). Virtual.
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2021 | Journal Article | IST-REx-ID: 14117 | OA
Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021). Toward causal representation learning. Proceedings of the IEEE. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/jproc.2021.3058954
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2021 | Conference Paper | IST-REx-ID: 14178 | OA
Dittadi, A., Träuble, F., Locatello, F., Wüthrich, M., Agrawal, V., Winther, O., … Schölkopf, B. (2021). On the transfer of disentangled representations in realistic settings. In The Ninth International Conference on Learning Representations. Virtual.
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2021 | Preprint | IST-REx-ID: 14221 | OA
Locatello, F. (n.d.). Enforcing and discovering structure in machine learning. arXiv. https://doi.org/10.48550/arXiv.2111.13693
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2021 | Conference Paper | IST-REx-ID: 14332
Träuble, F., Dittadi, A., Wuthrich, M., Widmaier, F., Gehler, P. V., Winther, O., … Bauer, S. (2021). Representation learning for out-of-distribution generalization in reinforcement learning. In ICML 2021 Workshop on Unsupervised Reinforcement Learning. Virtual.
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2020 | Journal Article | IST-REx-ID: 14125 | OA
Stark SG et al. 2020. SCIM: Universal single-cell matching with unpaired feature sets. Bioinformatics. 36(Supplement_2), i919–i927.
[Published Version] View | Files available | DOI | Download Published Version (ext.) | PubMed | Europe PMC
 

2020 | Conference Paper | IST-REx-ID: 14186 | OA
Locatello, F., Bauer, S., Lucic, M., Rätsch, G., Gelly, S., Schölkopf, B., & Bachem, O. (2020). A commentary on the unsupervised learning of disentangled representations. In The 34th AAAI Conference on Artificial Intelligence (Vol. 34, pp. 13681–13684). New York, NY, United States: Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v34i09.7120
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2020 | Conference Paper | IST-REx-ID: 14188 | OA
Locatello, F., Poole, B., Rätsch, G., Schölkopf, B., Bachem, O., & Tschannen, M. (2020). Weakly-supervised disentanglement without compromises. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 6348–6359). Virtual.
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2020 | Conference Paper | IST-REx-ID: 14187 | OA
Négiar, G., Dresdner, G., Tsai, A., Ghaoui, L. E., Locatello, F., Freund, R. M., & Pedregosa, F. (2020). Stochastic Frank-Wolfe for constrained finite-sum minimization. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 7253–7262). Virtual.
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2020 | Journal Article | IST-REx-ID: 14195 | OA
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. MIT Press.
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2020 | Conference Paper | IST-REx-ID: 14326 | OA
Locatello, F., Weissenborn, D., Unterthiner, T., Mahendran, A., Heigold, G., Uszkoreit, J., … Kipf, T. (2020). Object-centric learning with slot attention. In Advances in Neural Information Processing Systems (Vol. 33, pp. 11525–11538). Virtual: Curran Associates.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2019 | Conference Paper | IST-REx-ID: 14184 | OA
Locatello, F., Tschannen, M., Bauer, S., Rätsch, G., Schölkopf, B., & Bachem, O. (2019). Disentangling factors of variation using few labels. In 8th International Conference on Learning Representations. Virtual.
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2019 | Conference Paper | IST-REx-ID: 14189 | OA
Gresele, L., Rubenstein, P. K., Mehrjou, A., Locatello, F., & Schölkopf, B. (2019). The incomplete Rosetta Stone problem: Identifiability results for multi-view nonlinear ICA. In Proceedings of the 35th Conference on Uncertainty in Artificial  Intelligence (Vol. 115, pp. 217–227). Tel Aviv, Israel: ML Research Press.
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2019 | Conference Paper | IST-REx-ID: 14197 | OA
Locatello, F., Abbati, G., Rainforth, T., Bauer, S., Schölkopf, B., & Bachem, O. (2019). On the fairness of disentangled representations. In Advances in Neural Information Processing Systems (Vol. 32, pp. 14611–14624). Vancouver, Canada.
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2019 | Conference Paper | IST-REx-ID: 14191 | OA
Locatello, F., Yurtsever, A., Fercoq, O., & Cevher, V. (2019). Stochastic Frank-Wolfe for composite convex minimization. In Advances in Neural Information Processing Systems (Vol. 32, pp. 14291–14301). Vancouver, Canada.
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2019 | Conference Paper | IST-REx-ID: 14193 | OA
Steenkiste, S. van, Locatello, F., Schmidhuber, J., & Bachem, O. (2019). Are disentangled representations helpful for abstract visual reasoning? In Advances in Neural Information Processing Systems (Vol. 32). Vancouver, Canada.
[Preprint] View | Download Preprint (ext.) | arXiv
 

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