Francesco Locatello

Locatello Group

71 Publications

Mark all

[71]
2024 | Conference Paper | IST-REx-ID: 14213 | OA
Lao D, Hu Z, Locatello F, Yang Y, Soatto S. Divided attention: Unsupervised multi-object discovery with contextually separated slots. In: 1st Conference on Parsimony and Learning. ; 2024.
[Published Version] View | Files available | arXiv
 
[70]
2023 | Conference Paper | IST-REx-ID: 14105 | OA
Sinha S, Gehler P, Locatello F, Schiele B. TeST: Test-time Self-Training under distribution shift. In: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision. Institute of Electrical and Electronics Engineers; 2023. doi:10.1109/wacv56688.2023.00278
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[69]
2023 | Conference Paper | IST-REx-ID: 14208 | OA
Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. Benign overfitting in deep neural networks under lazy training. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:43105-43128.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[68]
2023 | Preprint | IST-REx-ID: 14209 | OA
Burg MF, Wenzel F, Zietlow D, et al. A data augmentation perspective on diffusion models and retrieval. arXiv. doi:10.48550/arXiv.2304.10253
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[67]
2023 | Conference Paper | IST-REx-ID: 14211 | OA
Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Causal discovery with score matching on additive models with arbitrary noise. In: 2nd Conference on Causal Learning and Reasoning. ; 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[66]
2023 | Conference Paper | IST-REx-ID: 14212 | OA
Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Scalable causal discovery with score matching. In: 2nd Conference on Causal Learning and Reasoning. ; 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[65]
2023 | Conference Paper | IST-REx-ID: 14214 | OA
Liu Y, Alahi A, Russell C, et al. Causal triplet: An open challenge for intervention-centric causal representation learning. In: 2nd Conference on Causal Learning and Reasoning. ; 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[64]
2023 | Conference Paper | IST-REx-ID: 14217 | OA
Moschella L, Maiorca V, Fumero M, Norelli A, Locatello F, Rodolà E. Relative representations enable zero-shot latent space communication. In: The 11th International Conference on Learning Representations. ; 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[63]
2023 | Conference Paper | IST-REx-ID: 14222 | OA
Tangemann M, Schneider S, Kügelgen J von, et al. Unsupervised object learning via common fate. In: 2nd Conference on Causal Learning and Reasoning. ; 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[62]
2023 | Conference Paper | IST-REx-ID: 14218 | OA
Seitzer M, Horn M, Zadaianchuk A, et al. Bridging the gap to real-world object-centric learning. In: The 11th International Conference on Learning Representations. ; 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[61]
2023 | Conference Paper | IST-REx-ID: 14219 | OA
Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. Unsupervised semantic segmentation with self-supervised object-centric representations. In: The 11th International Conference on Learning Representations. ; 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[60]
2023 | Preprint | IST-REx-ID: 14333 | OA
Faller PM, Vankadara LC, Mastakouri AA, Locatello F, Janzing D. Self-compatibility: Evaluating causal discovery without ground truth. arXiv. doi:10.48550/arXiv.2307.09552
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[59]
2023 | Journal Article | IST-REx-ID: 14949 | OA
Burg M, Wenzel F, Zietlow D, et al. Image retrieval outperforms diffusion models on data augmentation. Journal of Machine Learning Research. 2023.
[Published Version] View | Files available | Download Published Version (ext.)
 
[58]
2023 | Preprint | IST-REx-ID: 14946 | OA
Yao D, Xu D, Lachapelle S, et al. Multi-view causal representation learning with partial observability. arXiv. doi:10.48550/arXiv.2311.04056
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[57]
2023 | Preprint | IST-REx-ID: 14952 | OA
Maiorca V, Moschella L, Norelli A, Fumero M, Locatello F, Rodolà E. Latent space translation via semantic alignment. arXiv. doi:10.48550/arXiv.2311.00664
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[56]
2023 | Preprint | IST-REx-ID: 14948 | OA
Kori A, Locatello F, Ribeiro FDS, Toni F, Glocker B. Grounded object centric learning. arXiv. doi:10.48550/arXiv.2307.09437
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[55]
2023 | Preprint | IST-REx-ID: 14953 | OA
Zhu Z, Locatello F, Cevher V. Sample complexity bounds for score-matching: Causal discovery and generative modeling. arXiv. doi:10.48550/arXiv.2310.18123
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[54]
2023 | Preprint | IST-REx-ID: 14954 | OA
Montagna F, Mastakouri AA, Eulig E, et al. Assumption violations in causal discovery and the robustness of score matching. arXiv. doi:10.48550/arXiv.2310.13387
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[53]
2023 | Preprint | IST-REx-ID: 14210 | OA
Fumero M, Wenzel F, Zancato L, et al. Leveraging sparse and shared feature activations for disentangled representation learning. arXiv. doi:10.48550/arXiv.2304.07939
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[52]
2023 | Preprint | IST-REx-ID: 14207 | OA
Löwe S, Lippe P, Locatello F, Welling M. Rotating features for object discovery. arXiv. doi:10.48550/arXiv.2306.00600
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[51]
2023 | Preprint | IST-REx-ID: 14963 | OA
Zhao Z, Wang J, Horn M, et al. Object-centric multiple object tracking. arXiv. doi:10.48550/arXiv.2309.00233
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[50]
2023 | Preprint | IST-REx-ID: 14961 | OA
Montagna F, Noceti N, Rosasco L, Locatello F. Shortcuts for causal discovery of nonlinear models by score matching. arXiv. doi:10.48550/arXiv.2310.14246
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[49]
2023 | Preprint | IST-REx-ID: 14962 | OA
Fan K, Bai Z, Xiao T, et al. Unsupervised open-vocabulary object localization in videos. arXiv. doi:10.48550/arXiv.2309.09858
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[48]
2023 | Conference Paper | IST-REx-ID: 14958 | OA
Xu D, Yao D, Lachapelle S, et al. A sparsity principle for partially observable causal representation learning. In: Causal Representation Learning Workshop at NeurIPS 2023. OpenReview; 2023.
[Published Version] View | Files available | Download Published Version (ext.)
 
[47]
2023 | Conference Paper | IST-REx-ID: 14974 | OA
Zhang C, Janzing D, van der Schaar M, et al. Causality in the time of LLMs: Round table discussion results of CLeaR 2023. In: 2nd Conference on Causal Learning and Reasoning. ; 2023.
[Submitted Version] View | Files available
 
[46]
2022 | 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
 
[45]
2022 | Conference Paper | IST-REx-ID: 14106 | OA
Lohaus M, Kleindessner M, Kenthapadi K, Locatello F, Russell C. Are two heads the same as one? Identifying disparate treatment in fair neural networks. In: 36th Conference on Neural Information Processing Systems. Vol 35. Neural Information Processing Systems Foundation; 2022:16548-16562.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[44]
2022 | Conference Paper | IST-REx-ID: 14093 | OA
Dresdner G, Vladarean M-L, Rätsch G, Locatello F, Cevher V, Yurtsever A. Faster one-sample stochastic conditional gradient method for composite convex minimization. In: Proceedings of the 25th International Conference on Artificial Intelligence and Statistics. Vol 151. ML Research Press; 2022:8439-8457.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[43]
2022 | Conference Paper | IST-REx-ID: 14114 | OA
Zietlow D, Lohaus M, Balakrishnan G, et al. Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Institute of Electrical and Electronics Engineers; 2022:10400-10411. doi:10.1109/cvpr52688.2022.01016
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[42]
2022 | Conference Paper | IST-REx-ID: 14168 | OA
Rahaman N, Weiss M, Locatello F, et al. Neural attentive circuits. In: 36th Conference on Neural Information Processing Systems. Vol 35. ; 2022.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[41]
2022 | Conference Paper | IST-REx-ID: 14170 | OA
Dittadi A, Papa S, Vita MD, Schölkopf B, Winther O, Locatello F. Generalization and robustness implications in object-centric learning. In: Proceedings of the 39th International Conference on Machine Learning. Vol 2022. ML Research Press; :5221-5285.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[40]
2022 | 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
 
[39]
2022 | Conference Paper | IST-REx-ID: 14107 | OA
Yao J, Hong Y, Wang C, et al. Self-supervised amodal video object segmentation. In: 36th Conference on Neural Information Processing Systems. ; 2022. doi:10.48550/arXiv.2210.12733
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[38]
2022 | 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
 
[37]
2022 | 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
 
[36]
2022 | 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
 
[35]
2022 | 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
 
[34]
2022 | 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
 
[33]
2022 | Preprint | IST-REx-ID: 14216 | OA
Norelli A, Fumero M, Maiorca V, Moschella L, Rodolà E, Locatello F. ASIF: Coupled data turns unimodal models to multimodal without training. arXiv. doi:10.48550/arXiv.2210.01738
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[32]
2021 | 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
 
[31]
2021 | 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
 
[30]
2021 | 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
 
[29]
2021 | 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
 
[28]
2021 | 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
 
[27]
2021 | 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
 
[26]
2021 | 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
 
[25]
2021 | 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
 
[24]
2021 | 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
 
[23]
2021 | 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.
View
 
[22]
2020 | 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
 
[21]
2020 | 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
 
[20]
2020 | Conference Paper | IST-REx-ID: 14188 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[19]
2020 | Conference Paper | IST-REx-ID: 14187 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[18]
2020 | Journal Article | IST-REx-ID: 14195 | OA
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.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[17]
2020 | Conference Paper | IST-REx-ID: 14326 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[16]
2019 | Conference Paper | IST-REx-ID: 14184 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[15]
2019 | Conference Paper | IST-REx-ID: 14189 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[14]
2019 | Conference Paper | IST-REx-ID: 14197 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[13]
2019 | Conference Paper | IST-REx-ID: 14191 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[12]
2019 | Conference Paper | IST-REx-ID: 14193 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[11]
2019 | Conference Paper | IST-REx-ID: 14200 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[10]
2019 | Conference Paper | IST-REx-ID: 14190 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[9]
2018 | Conference Paper | IST-REx-ID: 14202 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[8]
2018 | Conference Paper | IST-REx-ID: 14201 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[7]
2018 | Conference Paper | IST-REx-ID: 14198 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[6]
2018 | Conference Paper | IST-REx-ID: 14203 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[5]
2018 | Conference Paper | IST-REx-ID: 14204 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[4]
2018 | Conference Paper | IST-REx-ID: 14224 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[3]
2018 | Preprint | IST-REx-ID: 14327 | OA
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
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[2]
2017 | Conference Paper | IST-REx-ID: 14206 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[1]
2017 | Conference Paper | IST-REx-ID: 14205 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
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71 Publications

Mark all

[71]
2024 | Conference Paper | IST-REx-ID: 14213 | OA
Lao D, Hu Z, Locatello F, Yang Y, Soatto S. Divided attention: Unsupervised multi-object discovery with contextually separated slots. In: 1st Conference on Parsimony and Learning. ; 2024.
[Published Version] View | Files available | arXiv
 
[70]
2023 | Conference Paper | IST-REx-ID: 14105 | OA
Sinha S, Gehler P, Locatello F, Schiele B. TeST: Test-time Self-Training under distribution shift. In: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision. Institute of Electrical and Electronics Engineers; 2023. doi:10.1109/wacv56688.2023.00278
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[69]
2023 | Conference Paper | IST-REx-ID: 14208 | OA
Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. Benign overfitting in deep neural networks under lazy training. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:43105-43128.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[68]
2023 | Preprint | IST-REx-ID: 14209 | OA
Burg MF, Wenzel F, Zietlow D, et al. A data augmentation perspective on diffusion models and retrieval. arXiv. doi:10.48550/arXiv.2304.10253
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[67]
2023 | Conference Paper | IST-REx-ID: 14211 | OA
Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Causal discovery with score matching on additive models with arbitrary noise. In: 2nd Conference on Causal Learning and Reasoning. ; 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[66]
2023 | Conference Paper | IST-REx-ID: 14212 | OA
Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Scalable causal discovery with score matching. In: 2nd Conference on Causal Learning and Reasoning. ; 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[65]
2023 | Conference Paper | IST-REx-ID: 14214 | OA
Liu Y, Alahi A, Russell C, et al. Causal triplet: An open challenge for intervention-centric causal representation learning. In: 2nd Conference on Causal Learning and Reasoning. ; 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[64]
2023 | Conference Paper | IST-REx-ID: 14217 | OA
Moschella L, Maiorca V, Fumero M, Norelli A, Locatello F, Rodolà E. Relative representations enable zero-shot latent space communication. In: The 11th International Conference on Learning Representations. ; 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[63]
2023 | Conference Paper | IST-REx-ID: 14222 | OA
Tangemann M, Schneider S, Kügelgen J von, et al. Unsupervised object learning via common fate. In: 2nd Conference on Causal Learning and Reasoning. ; 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[62]
2023 | Conference Paper | IST-REx-ID: 14218 | OA
Seitzer M, Horn M, Zadaianchuk A, et al. Bridging the gap to real-world object-centric learning. In: The 11th International Conference on Learning Representations. ; 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[61]
2023 | Conference Paper | IST-REx-ID: 14219 | OA
Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. Unsupervised semantic segmentation with self-supervised object-centric representations. In: The 11th International Conference on Learning Representations. ; 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[60]
2023 | Preprint | IST-REx-ID: 14333 | OA
Faller PM, Vankadara LC, Mastakouri AA, Locatello F, Janzing D. Self-compatibility: Evaluating causal discovery without ground truth. arXiv. doi:10.48550/arXiv.2307.09552
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[59]
2023 | Journal Article | IST-REx-ID: 14949 | OA
Burg M, Wenzel F, Zietlow D, et al. Image retrieval outperforms diffusion models on data augmentation. Journal of Machine Learning Research. 2023.
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[58]
2023 | Preprint | IST-REx-ID: 14946 | OA
Yao D, Xu D, Lachapelle S, et al. Multi-view causal representation learning with partial observability. arXiv. doi:10.48550/arXiv.2311.04056
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[57]
2023 | Preprint | IST-REx-ID: 14952 | OA
Maiorca V, Moschella L, Norelli A, Fumero M, Locatello F, Rodolà E. Latent space translation via semantic alignment. arXiv. doi:10.48550/arXiv.2311.00664
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[56]
2023 | Preprint | IST-REx-ID: 14948 | OA
Kori A, Locatello F, Ribeiro FDS, Toni F, Glocker B. Grounded object centric learning. arXiv. doi:10.48550/arXiv.2307.09437
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[55]
2023 | Preprint | IST-REx-ID: 14953 | OA
Zhu Z, Locatello F, Cevher V. Sample complexity bounds for score-matching: Causal discovery and generative modeling. arXiv. doi:10.48550/arXiv.2310.18123
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[54]
2023 | Preprint | IST-REx-ID: 14954 | OA
Montagna F, Mastakouri AA, Eulig E, et al. Assumption violations in causal discovery and the robustness of score matching. arXiv. doi:10.48550/arXiv.2310.13387
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[53]
2023 | Preprint | IST-REx-ID: 14210 | OA
Fumero M, Wenzel F, Zancato L, et al. Leveraging sparse and shared feature activations for disentangled representation learning. arXiv. doi:10.48550/arXiv.2304.07939
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[52]
2023 | Preprint | IST-REx-ID: 14207 | OA
Löwe S, Lippe P, Locatello F, Welling M. Rotating features for object discovery. arXiv. doi:10.48550/arXiv.2306.00600
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[51]
2023 | Preprint | IST-REx-ID: 14963 | OA
Zhao Z, Wang J, Horn M, et al. Object-centric multiple object tracking. arXiv. doi:10.48550/arXiv.2309.00233
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[50]
2023 | Preprint | IST-REx-ID: 14961 | OA
Montagna F, Noceti N, Rosasco L, Locatello F. Shortcuts for causal discovery of nonlinear models by score matching. arXiv. doi:10.48550/arXiv.2310.14246
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[49]
2023 | Preprint | IST-REx-ID: 14962 | OA
Fan K, Bai Z, Xiao T, et al. Unsupervised open-vocabulary object localization in videos. arXiv. doi:10.48550/arXiv.2309.09858
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[48]
2023 | Conference Paper | IST-REx-ID: 14958 | OA
Xu D, Yao D, Lachapelle S, et al. A sparsity principle for partially observable causal representation learning. In: Causal Representation Learning Workshop at NeurIPS 2023. OpenReview; 2023.
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[47]
2023 | Conference Paper | IST-REx-ID: 14974 | OA
Zhang C, Janzing D, van der Schaar M, et al. Causality in the time of LLMs: Round table discussion results of CLeaR 2023. In: 2nd Conference on Causal Learning and Reasoning. ; 2023.
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[46]
2022 | 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
 
[45]
2022 | Conference Paper | IST-REx-ID: 14106 | OA
Lohaus M, Kleindessner M, Kenthapadi K, Locatello F, Russell C. Are two heads the same as one? Identifying disparate treatment in fair neural networks. In: 36th Conference on Neural Information Processing Systems. Vol 35. Neural Information Processing Systems Foundation; 2022:16548-16562.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[44]
2022 | Conference Paper | IST-REx-ID: 14093 | OA
Dresdner G, Vladarean M-L, Rätsch G, Locatello F, Cevher V, Yurtsever A. Faster one-sample stochastic conditional gradient method for composite convex minimization. In: Proceedings of the 25th International Conference on Artificial Intelligence and Statistics. Vol 151. ML Research Press; 2022:8439-8457.
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[43]
2022 | Conference Paper | IST-REx-ID: 14114 | OA
Zietlow D, Lohaus M, Balakrishnan G, et al. Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Institute of Electrical and Electronics Engineers; 2022:10400-10411. doi:10.1109/cvpr52688.2022.01016
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[42]
2022 | Conference Paper | IST-REx-ID: 14168 | OA
Rahaman N, Weiss M, Locatello F, et al. Neural attentive circuits. In: 36th Conference on Neural Information Processing Systems. Vol 35. ; 2022.
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[41]
2022 | Conference Paper | IST-REx-ID: 14170 | OA
Dittadi A, Papa S, Vita MD, Schölkopf B, Winther O, Locatello F. Generalization and robustness implications in object-centric learning. In: Proceedings of the 39th International Conference on Machine Learning. Vol 2022. ML Research Press; :5221-5285.
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[40]
2022 | 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.
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[39]
2022 | Conference Paper | IST-REx-ID: 14107 | OA
Yao J, Hong Y, Wang C, et al. Self-supervised amodal video object segmentation. In: 36th Conference on Neural Information Processing Systems. ; 2022. doi:10.48550/arXiv.2210.12733
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[38]
2022 | 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.
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[37]
2022 | 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
 
[36]
2022 | 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
 
[35]
2022 | 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
 
[34]
2022 | 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
 
[33]
2022 | Preprint | IST-REx-ID: 14216 | OA
Norelli A, Fumero M, Maiorca V, Moschella L, Rodolà E, Locatello F. ASIF: Coupled data turns unimodal models to multimodal without training. arXiv. doi:10.48550/arXiv.2210.01738
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[32]
2021 | 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
 
[31]
2021 | 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
 
[30]
2021 | 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
 
[29]
2021 | 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
 
[28]
2021 | 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
 
[27]
2021 | 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
 
[26]
2021 | 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
 
[25]
2021 | 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
 
[24]
2021 | 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
 
[23]
2021 | 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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[22]
2020 | 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
 
[21]
2020 | 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
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[20]
2020 | Conference Paper | IST-REx-ID: 14188 | OA
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.
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[19]
2020 | Conference Paper | IST-REx-ID: 14187 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[18]
2020 | Journal Article | IST-REx-ID: 14195 | OA
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.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[17]
2020 | Conference Paper | IST-REx-ID: 14326 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[16]
2019 | Conference Paper | IST-REx-ID: 14184 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[15]
2019 | Conference Paper | IST-REx-ID: 14189 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[14]
2019 | Conference Paper | IST-REx-ID: 14197 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[13]
2019 | Conference Paper | IST-REx-ID: 14191 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[12]
2019 | Conference Paper | IST-REx-ID: 14193 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[11]
2019 | Conference Paper | IST-REx-ID: 14200 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[10]
2019 | Conference Paper | IST-REx-ID: 14190 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[9]
2018 | Conference Paper | IST-REx-ID: 14202 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[8]
2018 | Conference Paper | IST-REx-ID: 14201 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[7]
2018 | Conference Paper | IST-REx-ID: 14198 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[6]
2018 | Conference Paper | IST-REx-ID: 14203 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[5]
2018 | Conference Paper | IST-REx-ID: 14204 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[4]
2018 | Conference Paper | IST-REx-ID: 14224 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[3]
2018 | Preprint | IST-REx-ID: 14327 | OA
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
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[2]
2017 | Conference Paper | IST-REx-ID: 14206 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[1]
2017 | Conference Paper | IST-REx-ID: 14205 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
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