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88 Publications
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2021 | Published | Conference Paper | IST-REx-ID: 14180 | 
N. Rahaman et al., “Dynamic inference with neural interpreters,” in Advances in Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 10985–10998.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 14181 |
G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, and G. Rätsch, “Boosting variational inference with locally adaptive step-sizes,” in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Montreal, Canada, 2021, pp. 2337–2343.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 14182 |
F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, and P. Gehler, “Backward-compatible prediction updates: A probabilistic approach,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 116–128.
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| arXiv
2021 | Submitted | Preprint | IST-REx-ID: 14221 |
F. Locatello, “Enforcing and discovering structure in machine learning,” arXiv. .
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 14332
F. Träuble et al., “Representation learning for out-of-distribution generalization in reinforcement learning,” in ICML 2021 Workshop on Unsupervised Reinforcement Learning, Virtual, 2021.
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2021 | Patent | IST-REx-ID: 14185 |
D. Weissenborn et al., “Object-centric learning with slot attention.” 2021.
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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 |
F. Locatello et al., “A commentary on the unsupervised learning of disentangled representations,” in The 34th AAAI Conference on Artificial Intelligence, New York, NY, United States, 2020, vol. 34, no. 9, pp. 13681–13684.
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| arXiv
2020 | Published | Conference Paper | IST-REx-ID: 14187 |
G. Négiar et al., “Stochastic Frank-Wolfe for constrained finite-sum minimization,” in Proceedings of the 37th International Conference on Machine Learning, Virtual, 2020, vol. 119, pp. 7253–7262.
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| arXiv
2020 | Published | Conference Paper | IST-REx-ID: 14188 |
F. Locatello, B. Poole, G. Rätsch, B. Schölkopf, O. Bachem, and M. Tschannen, “Weakly-supervised disentanglement without compromises,” in Proceedings of the 37th International Conference on Machine Learning, Virtual, 2020, vol. 119, pp. 6348–6359.
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| arXiv
2020 | Published | Journal Article | IST-REx-ID: 14195 |
F. Locatello et al., “A sober look at the unsupervised learning of disentangled representations and their evaluation,” Journal of Machine Learning Research, vol. 21. MIT Press, 2020.
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| arXiv
2020 | Published | Conference Paper | IST-REx-ID: 14326 |
F. Locatello et al., “Object-centric learning with slot attention,” in 34th International Conference on Neural Information Processing Systems, Virtual, 2020, vol. 33, pp. 11525–11538.
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| arXiv
2019 | Published | Conference Paper | IST-REx-ID: 14184 |
F. Locatello, M. Tschannen, S. Bauer, G. Rätsch, B. Schölkopf, and O. Bachem, “Disentangling factors of variation using few labels,” in 8th International Conference on Learning Representations, Virtual, 2019.
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| arXiv
2019 | Published | Conference Paper | IST-REx-ID: 14189 |
L. Gresele, P. K. Rubenstein, A. Mehrjou, F. Locatello, and B. Schölkopf, “The incomplete Rosetta Stone problem: Identifiability results for multi-view nonlinear ICA,” in Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence, Tel Aviv, Israel, 2019, vol. 115, pp. 217–227.
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| arXiv
2019 | Published | Conference Paper | IST-REx-ID: 14190 |
M. W. Gondal et al., “On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32.
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| arXiv
2019 | Published | Conference Paper | IST-REx-ID: 14191 |
F. Locatello, A. Yurtsever, O. Fercoq, and V. Cevher, “Stochastic Frank-Wolfe for composite convex minimization,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32, pp. 14291–14301.
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| arXiv
2019 | Published | Conference Paper | IST-REx-ID: 14193 |
S. van Steenkiste, F. Locatello, J. Schmidhuber, and O. Bachem, “Are disentangled representations helpful for abstract visual reasoning?,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32.
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| arXiv
2019 | Published | Conference Paper | IST-REx-ID: 14197 |
F. Locatello, G. Abbati, T. Rainforth, S. Bauer, B. Schölkopf, and O. Bachem, “On the fairness of disentangled representations,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32, pp. 14611–14624.
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| arXiv
2019 | Published | Conference Paper | IST-REx-ID: 14200 |
F. Locatello et al., “Challenging common assumptions in the unsupervised learning of disentangled representations,” in Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, United States, 2019, vol. 97, pp. 4114–4124.
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| arXiv
2018 | Published | Conference Paper | IST-REx-ID: 14198 |
V. Fortuin, M. Hüser, F. Locatello, H. Strathmann, and G. Rätsch, “SOM-VAE: Interpretable discrete representation learning on time series,” in International Conference on Learning Representations, New Orleans, LA, United States, 2018.
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| arXiv
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