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Marco Fumero

Riccardo Cadei

Francesco Locatello

Berker Demirel

Dingling Yao

Shimeng Huang

Clementine Domine

Adeel Pervez

Walter Nelson

Francesco Montagna

Dario Rancati



87 Publications

2022 | Published | Conference Paper | IST-REx-ID: 14174 | OA
The role of pretrained representations for the OOD generalization of reinforcement learning agents
A. Dittadi, F. Träuble, M. Wüthrich, F. Widmaier, P. Gehler, O. Winther, F. Locatello, O. Bachem, B. Schölkopf, S. Bauer, in:, 10th International Conference on Learning Representations, 2022.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2022 | Published | Conference Paper | IST-REx-ID: 14175 | OA
You mostly walk alone: Analyzing feature attribution in trajectory prediction
O. Makansi, J. von Kügelgen, F. Locatello, P. Gehler, D. Janzing, T. Brox, B. Schölkopf, in:, 10th International Conference on Learning Representations, 2022.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2022 | Submitted | Conference Paper | IST-REx-ID: 14215 | OA
A general purpose neural architecture for geospatial systems
N. Rahaman, M. Weiss, F. Träuble, F. Locatello, A. Lacoste, Y. Bengio, C. Pal, L.E. Li, B. Schölkopf, in:, 36th Conference on Neural Information Processing Systems, n.d.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2022 | Submitted | Preprint | IST-REx-ID: 14220 | OA
Compositional multi-object reinforcement learning with linear relation networks
D. Mambelli, F. Träuble, S. Bauer, B. Schölkopf, F. Locatello, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2021 | Published | Journal Article | IST-REx-ID: 14117 | OA
Toward causal representation learning
B. Scholkopf, F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, Y. Bengio, Proceedings of the IEEE 109 (2021) 612–634.
[Published Version] View | DOI | Download Published Version (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 14176 | OA
Neighborhood contrastive learning applied to online patient monitoring
H. Yèche, G. Dresdner, F. Locatello, M. Hüser, G. Rätsch, in:, Proceedings of 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 11964–11974.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 14177 | OA
On disentangled representations learned from correlated data
F. Träuble, E. Creager, N. Kilbertus, F. Locatello, A. Dittadi, A. Goyal, B. Schölkopf, S. Bauer, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 10401–10412.
[Published Version] View | Download Published Version (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 14178 | OA
On the transfer of disentangled representations in realistic settings
A. Dittadi, F. Träuble, F. Locatello, M. Wüthrich, V. Agrawal, O. Winther, S. Bauer, B. Schölkopf, in:, The Ninth International Conference on Learning Representations, 2021.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 14179 | OA
Self-supervised learning with data augmentations provably isolates content from style
J. von Kügelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve, F. Locatello, in:, Advances in Neural Information Processing Systems, 2021, pp. 16451–16467.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 14180 | OA
Dynamic inference with neural interpreters
N. Rahaman, M.W. Gondal, S. Joshi, P. Gehler, Y. Bengio, F. Locatello, B. Schölkopf, in:, Advances in Neural Information Processing Systems, 2021, pp. 10985–10998.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 14181 | OA
Boosting variational inference with locally adaptive step-sizes
G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, G. Rätsch, in:, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, 2021, pp. 2337–2343.
[Published Version] View | DOI | Download Published Version (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 14182 | OA
Backward-compatible prediction updates: A probabilistic approach
F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, P. Gehler, in:, 35th Conference on Neural Information Processing Systems, 2021, pp. 116–128.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2021 | Submitted | Preprint | IST-REx-ID: 14221 | OA
Enforcing and discovering structure in machine learning
F. Locatello, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 14332
Representation learning for out-of-distribution generalization in reinforcement learning
F. Träuble, A. Dittadi, M. Wuthrich, F. Widmaier, P.V. Gehler, O. Winther, F. Locatello, O. Bachem, B. Schölkopf, S. Bauer, in:, ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021.
View
 
2021 | Patent | IST-REx-ID: 14185 | OA
Object-centric learning with slot attention
D. Weissenborn, J. Uszkoreit, T. Unterthiner, A. Mahendran, F. Locatello, T. Kipf, G. Heigold, A. Dosovitskiy, (2021).
[Published Version] View | Download Published Version (ext.) | arXiv
 
2020 | Published | Journal Article | IST-REx-ID: 14125 | OA
SCIM: Universal single-cell matching with unpaired feature sets
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
A commentary on the unsupervised learning of disentangled representations
F. Locatello, S. Bauer, M. Lucic, G. Rätsch, S. Gelly, B. Schölkopf, O. Bachem, in:, The 34th AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence, 2020, pp. 13681–13684.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2020 | Published | Conference Paper | IST-REx-ID: 14187 | OA
Stochastic Frank-Wolfe for constrained finite-sum minimization
G. Négiar, G. Dresdner, A. Tsai, L.E. Ghaoui, F. Locatello, R.M. Freund, F. Pedregosa, in:, Proceedings of the 37th International Conference on Machine Learning, 2020, pp. 7253–7262.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2020 | Published | Conference Paper | IST-REx-ID: 14188 | OA
Weakly-supervised disentanglement without compromises
F. Locatello, B. Poole, G. Rätsch, B. Schölkopf, O. Bachem, M. Tschannen, in:, Proceedings of the 37th International Conference on Machine Learning, 2020, pp. 6348–6359.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2020 | Published | Journal Article | IST-REx-ID: 14195 | OA
A sober look at the unsupervised learning of disentangled representations and their evaluation
F. Locatello, S. Bauer, M. Lucic, G. Rätsch, S. Gelly, B. Schölkopf, O. Bachem, Journal of Machine Learning Research 21 (2020).
[Published Version] View | Download Published Version (ext.) | arXiv
 

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