Yèche H, Dresdner G, Locatello F, Hüser M, Rätsch G. Neighborhood contrastive learning applied to online patient monitoring. In: Proceedings of 38th International Conference on Machine Learning. Vol 139. ML Research Press; 2021:11964-11974.
LibreCat: https://research-explorer.ista.ac.at/record/14176
arXiv: 2106.05142

Träuble F, Kügelgen J von, Kleindessner M, Locatello F, Schölkopf B, Gehler P. Backward-compatible prediction updates: A probabilistic approach. In: 35th Conference on Neural Information Processing Systems. Vol 34. ; 2021:116-128.
LibreCat: https://research-explorer.ista.ac.at/record/14182
arXiv: 2107.01057

Dresdner G, Shekhar S, Pedregosa F, Locatello F, Rätsch G. Boosting variational inference with locally adaptive step-sizes. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence; 2021:2337-2343. doi:10.24963/ijcai.2021/322
LibreCat: https://research-explorer.ista.ac.at/record/14181
DOI: 10.24963/ijcai.2021/322
arXiv: 2105.09240

Kügelgen J von, Sharma Y, Gresele L, et al. Self-supervised learning with data augmentations provably isolates content from style. In: Advances in Neural Information Processing Systems. Vol 34. ; 2021:16451-16467.
LibreCat: https://research-explorer.ista.ac.at/record/14179
arXiv: 2106.04619

Rahaman N, Gondal MW, Joshi S, et al. Dynamic inference with neural interpreters. In: Advances in Neural Information Processing Systems. Vol 34. ; 2021:10985-10998.
LibreCat: https://research-explorer.ista.ac.at/record/14180
arXiv: 2110.06399

Scholkopf B, Locatello F, Bauer S, et al. Toward causal representation learning. Proceedings of the IEEE. 2021;109(5):612-634. doi:10.1109/jproc.2021.3058954
LibreCat: https://research-explorer.ista.ac.at/record/14117
DOI: 10.1109/jproc.2021.3058954
arXiv: 2102.11107

Dittadi A, Träuble F, Locatello F, et al. On the transfer of disentangled representations in realistic settings. In: The Ninth International Conference on Learning Representations. ; 2021.
LibreCat: https://research-explorer.ista.ac.at/record/14178
arXiv: 2010.14407

Locatello F. Enforcing and discovering structure in machine learning. arXiv. doi:10.48550/arXiv.2111.13693
LibreCat: https://research-explorer.ista.ac.at/record/14221
DOI: 10.48550/arXiv.2111.13693
arXiv: 2111.13693

Träuble F, Dittadi A, Wuthrich M, et al. Representation learning for out-of-distribution generalization in reinforcement learning. In: ICML 2021 Workshop on Unsupervised Reinforcement Learning. ; 2021.
LibreCat: https://research-explorer.ista.ac.at/record/14332

Stark SG, Ficek J, Locatello F, et al. SCIM: Universal single-cell matching with unpaired feature sets. Bioinformatics. 2020;36(Supplement_2):i919-i927. doi:10.1093/bioinformatics/btaa843
LibreCat: https://research-explorer.ista.ac.at/record/14125
DOI: 10.1093/bioinformatics/btaa843
PMID: 33381818

Locatello F, Bauer S, Lucic M, et al. A commentary on the unsupervised learning of disentangled representations. In: The 34th AAAI Conference on Artificial Intelligence. Vol 34. Association for the Advancement of Artificial Intelligence; 2020:13681-13684. doi:10.1609/aaai.v34i09.7120
LibreCat: https://research-explorer.ista.ac.at/record/14186
DOI: 10.1609/aaai.v34i09.7120
arXiv: 2007.14184

Locatello F, Poole B, Rätsch G, Schölkopf B, Bachem O, Tschannen M. Weakly-supervised disentanglement without compromises. In: Proceedings of the 37th International Conference on Machine Learning. Vol 119. ; 2020:6348–6359.
LibreCat: https://research-explorer.ista.ac.at/record/14188
arXiv: 2002.02886

Négiar G, Dresdner G, Tsai A, et al. Stochastic Frank-Wolfe for constrained finite-sum minimization. In: Proceedings of the 37th International Conference on Machine Learning. Vol 119. ; 2020:7253-7262.
LibreCat: https://research-explorer.ista.ac.at/record/14187
arXiv: 2002.11860

Locatello F, Bauer S, Lucic M, et al. A sober look at the unsupervised learning of disentangled representations and their evaluation. Journal of Machine Learning Research. 2020;21.
LibreCat: https://research-explorer.ista.ac.at/record/14195
arXiv: 2010.14766

Locatello F, Weissenborn D, Unterthiner T, et al. Object-centric learning with slot attention. In: Advances in Neural Information Processing Systems. Vol 33. Curran Associates; 2020:11525-11538.
LibreCat: https://research-explorer.ista.ac.at/record/14326
arXiv: 2006.15055

Locatello F, Tschannen M, Bauer S, Rätsch G, Schölkopf B, Bachem O. Disentangling factors of variation using few labels. In: 8th International Conference on Learning Representations. ; 2019.
LibreCat: https://research-explorer.ista.ac.at/record/14184
arXiv: 1905.01258

Gresele L, Rubenstein PK, Mehrjou A, Locatello F, Schölkopf B. 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. ML Research Press; 2019:217-227.
LibreCat: https://research-explorer.ista.ac.at/record/14189
arXiv: 1905.06642

Locatello F, Abbati G, Rainforth T, Bauer S, Schölkopf B, Bachem O. On the fairness of disentangled representations. In: Advances in Neural Information Processing Systems. Vol 32. ; 2019:14611–14624.
LibreCat: https://research-explorer.ista.ac.at/record/14197
arXiv: 1905.13662

Locatello F, Yurtsever A, Fercoq O, Cevher V. Stochastic Frank-Wolfe for composite convex minimization. In: Advances in Neural Information Processing Systems. Vol 32. ; 2019:14291–14301.
LibreCat: https://research-explorer.ista.ac.at/record/14191
arXiv: 1901.10348

Steenkiste S van, Locatello F, Schmidhuber J, Bachem O. Are disentangled representations helpful for abstract visual reasoning? In: Advances in Neural Information Processing Systems. Vol 32. ; 2019.
LibreCat: https://research-explorer.ista.ac.at/record/14193
arXiv: 1905.12506