Please note that LibreCat no longer supports Internet Explorer versions 8 or 9 (or earlier).
We recommend upgrading to the latest Internet Explorer, Google Chrome, or Firefox.
87 Publications
- 1
- 2
- 3 (current)
- 4
- 5
2022 | Published | Conference Paper | IST-REx-ID: 14174 | 
Dittadi, A., Träuble, F., Wüthrich, M., Widmaier, F., Gehler, P., Winther, O., … Bauer, S. (2022). The role of pretrained representations for the OOD generalization of reinforcement learning agents. In 10th International Conference on Learning Representations. Virtual.
[Preprint]
View
| Download Preprint (ext.)
| 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. In 10th International Conference on Learning Representations. Virtual.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2022 | Submitted | Conference Paper | IST-REx-ID: 14215 |
Rahaman, N., Weiss, M., Träuble, F., Locatello, F., Lacoste, A., Bengio, Y., … Schölkopf, B. (n.d.). A general purpose neural architecture for geospatial systems. In 36th Conference on Neural Information Processing Systems. New Orleans, LA, United States.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2022 | Submitted | Preprint | IST-REx-ID: 14220 |
Mambelli, D., Träuble, F., Bauer, S., Schölkopf, B., & Locatello, F. (n.d.). Compositional multi-object reinforcement learning with linear relation networks. arXiv. https://doi.org/10.48550/arXiv.2201.13388
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2021 | Published | Journal Article | IST-REx-ID: 14117 |
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
[Published Version]
View
| DOI
| Download Published Version (ext.)
| 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. 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 | Published | Conference Paper | IST-REx-ID: 14177 |
Träuble, F., Creager, E., Kilbertus, N., Locatello, F., Dittadi, A., Goyal, A., … Bauer, S. (2021). On disentangled representations learned from correlated data. In Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 10401–10412). Virtual: ML Research Press.
[Published Version]
View
| Download Published Version (ext.)
| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 14178 |
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.
[Preprint]
View
| Download Preprint (ext.)
| 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. In Advances in Neural Information Processing Systems (Vol. 34, pp. 16451–16467). Virtual.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 14180 |
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.
[Preprint]
View
| Download Preprint (ext.)
| 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. 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
[Published Version]
View
| DOI
| Download Published Version (ext.)
| 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. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 116–128). Virtual.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2021 | Submitted | Preprint | IST-REx-ID: 14221 |
Locatello, F. (n.d.). Enforcing and discovering structure in machine learning. arXiv. https://doi.org/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., 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.
View
2021 | Patent | IST-REx-ID: 14185 |
Weissenborn, D., Uszkoreit, J., Unterthiner, T., Mahendran, A., Locatello, F., Kipf, T., … Dosovitskiy, A. (2021). Object-centric learning with slot attention.
[Published Version]
View
| Download Published Version (ext.)
| 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.
[Published Version]
View
| Files available
| DOI
| Download Published Version (ext.)
| 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. 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
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2020 | Published | Conference Paper | IST-REx-ID: 14187 |
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.
[Preprint]
View
| Download Preprint (ext.)
| 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. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 6348–6359). Virtual.
[Preprint]
View
| Download Preprint (ext.)
| 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. MIT Press.
[Published Version]
View
| Download Published Version (ext.)
| arXiv
- 1
- 2
- 3 (current)
- 4
- 5