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Berker Demirel

Francesco Locatello

Marco Fumero

Adeel Pervez

Walter Nelson

Francesco Montagna

Riccardo Cadei

Sarah Mameche

Dario Rancati

Valentino Maiorca

Shimeng Huang

Dingling Yao



100 Publications

2022 | Published | Conference Paper | IST-REx-ID: 14171 | OA
Rolland P, Cevher V, Kleindessner M, et al. Score matching enables causal discovery of nonlinear additive noise  models. In: Proceedings of the 39th International Conference on Machine Learning. Vol 162. ML Research Press; 2022:18741-18753.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2022 | Published | Conference Paper | IST-REx-ID: 14172 | OA
Schott L, Kügelgen J von, Träuble F, et al. Visual representation learning does not generalize strongly within the  same domain. In: 10th International Conference on Learning Representations. ; 2022.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2022 | Published | Conference Paper | IST-REx-ID: 14173 | OA
Wenzel F, Dittadi A, Gehler PV, et al. Assaying out-of-distribution generalization in transfer learning. In: 36th Conference on Neural Information Processing Systems. Vol 35. Neural Information Processing Systems Foundation; 2022:7181-7198.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2022 | Published | Conference Paper | IST-REx-ID: 14174 | OA
Dittadi A, Träuble F, Wüthrich M, et al. The role of pretrained representations for the OOD generalization of  reinforcement learning agents. In: 10th International Conference on Learning Representations. ; 2022.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2022 | Published | Conference Paper | IST-REx-ID: 14175 | OA
Makansi O, Kügelgen J von, Locatello F, et al. You mostly walk alone: Analyzing feature attribution in trajectory prediction. In: 10th International Conference on Learning Representations. ; 2022.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2022 | Submitted | Conference Paper | IST-REx-ID: 14215 | OA
Rahaman N, Weiss M, Träuble F, et al. A general purpose neural architecture for geospatial systems. In: 36th Conference on Neural Information Processing Systems.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2022 | Submitted | Preprint | IST-REx-ID: 14220 | OA
Mambelli D, Träuble F, Bauer S, Schölkopf B, Locatello F. Compositional multi-object reinforcement learning with linear relation networks. arXiv. doi:10.48550/arXiv.2201.13388
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2021 | Published | Journal Article | IST-REx-ID: 14117 | OA
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
[Published Version] View | DOI | Download Published Version (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 14176 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 14177 | OA
Träuble F, Creager E, Kilbertus N, et al. On disentangled representations learned from correlated data. In: Proceedings of the 38th International Conference on Machine Learning. Vol 139. ML Research Press; 2021:10401-10412.
[Published Version] View | Download Published Version (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 14178 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 14179 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 14180 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 14181 | OA
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
[Published Version] View | DOI | Download Published Version (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 14182 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2021 | Patent | IST-REx-ID: 14185 | OA
Weissenborn D, Uszkoreit J, Unterthiner T, et al. Object-centric learning with slot attention. 2021.
[Published Version] View | Download Published Version (ext.) | arXiv
 
2021 | Submitted | Preprint | IST-REx-ID: 14221 | OA
Locatello F. Enforcing and discovering structure in machine learning. arXiv. doi:10.48550/arXiv.2111.13693
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 14332
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.
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2020 | Published | 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 | Published | Conference Paper | IST-REx-ID: 14186 | OA
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
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

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