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87 Publications
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2020 | Published | Conference Paper | IST-REx-ID: 14326 | 
Locatello F, Weissenborn D, Unterthiner T, et al. Object-centric learning with slot attention. In: 34th International Conference on Neural Information Processing Systems. Vol 33. Neural Information Processing Systems Foundation; 2020:11525-11538.
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| arXiv
2019 | Published | Conference Paper | IST-REx-ID: 14184 |
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.
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| arXiv
2019 | Published | Conference Paper | IST-REx-ID: 14189 |
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.
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| arXiv
2019 | Published | Conference Paper | IST-REx-ID: 14190 |
Gondal MW, Wüthrich M, Miladinović Đ, 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. Vol 32. ; 2019.
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| arXiv
2019 | Published | Conference Paper | IST-REx-ID: 14191 |
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.
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| arXiv
2019 | Published | Conference Paper | IST-REx-ID: 14193 |
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.
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| arXiv
2019 | Published | Conference Paper | IST-REx-ID: 14197 |
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.
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| arXiv
2019 | Published | Conference Paper | IST-REx-ID: 14200 |
Locatello F, Bauer S, Lucic M, et al. Challenging common assumptions in the unsupervised learning of disentangled representations. In: Proceedings of the 36th International Conference on Machine Learning. Vol 97. ML Research Press; 2019:4114-4124.
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| arXiv
2018 | Published | Conference Paper | IST-REx-ID: 14198 |
Fortuin V, Hüser M, Locatello F, Strathmann H, Rätsch G. SOM-VAE: Interpretable discrete representation learning on time series. In: International Conference on Learning Representations. ; 2018.
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| arXiv
2018 | Published | Conference Paper | IST-REx-ID: 14201 |
Locatello F, Khanna R, Ghosh J, Rätsch G. Boosting variational inference: An optimization perspective. In: Proceedings of the 21st International Conference on Artificial Intelligence and Statistics. Vol 84. ML Research Press; 2018:464-472.
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| arXiv
2018 | Published | Conference Paper | IST-REx-ID: 14202 |
Locatello F, Dresdner G, Khanna R, Valera I, Rätsch G. Boosting black box variational inference. In: Advances in Neural Information Processing Systems. Vol 31. Neural Information Processing Systems Foundation; 2018.
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| arXiv
2018 | Published | Conference Paper | IST-REx-ID: 14203 |
Yurtsever A, Fercoq O, Locatello F, Cevher V. A conditional gradient framework for composite convex minimization with applications to semidefinite programming. In: Proceedings of the 35th International Conference on Machine Learning. Vol 80. ML Research Press; 2018:5727-5736.
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| arXiv
2018 | Published | Conference Paper | IST-REx-ID: 14204 |
Locatello F, Raj A, Karimireddy SP, et al. On matching pursuit and coordinate descent. In: Proceedings of the 35th International Conference on Machine Learning. Vol 80. ML Research Press; 2018:3198-3207.
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| arXiv
2018 | Published | Conference Paper | IST-REx-ID: 14224 |
Locatello F, Vincent D, Tolstikhin I, Ratsch G, Gelly S, Scholkopf B. Clustering meets implicit generative models. In: 6th International Conference on Learning Representations. ; 2018.
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| arXiv
2018 | Submitted | Preprint | IST-REx-ID: 14327 |
Locatello F, Vincent D, Tolstikhin I, Rätsch G, Gelly S, Schölkopf B. Competitive training of mixtures of independent deep generative models. arXiv. doi:10.48550/arXiv.1804.11130
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| arXiv
2017 | Published | Conference Paper | IST-REx-ID: 14205 |
Locatello F, Khanna R, Tschannen M, Jaggi M. A unified optimization view on generalized matching pursuit and Frank-Wolfe. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. Vol 54. ML Research Press; 2017:860-868.
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| arXiv
2017 | Published | Conference Paper | IST-REx-ID: 14206 |
Locatello F, Tschannen M, Rätsch G, Jaggi M. Greedy algorithms for cone constrained optimization with convergence guarantees. In: Advances in Neural Information Processing Systems. ; 2017.
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| arXiv
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