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.
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arXiv: 2304.01430

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
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arXiv: 2209.11459

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.
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arXiv: 2305.19377

Burg MF, Wenzel F, Zietlow D, et al. A data augmentation perspective on diffusion models and retrieval. arXiv. doi:10.48550/arXiv.2304.10253
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arXiv: 2304.10253

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.
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arXiv: 2304.03265

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.
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arXiv: 2304.03382

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.
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arXiv: 2301.05169

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.
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arXiv: 2209.15430

Tangemann M, Schneider S, Kügelgen J von, et al. Unsupervised object learning via common fate. In: 2nd Conference on Causal Learning and Reasoning. ; 2023.
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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.
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arXiv: 2209.14860

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.
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arXiv: 2207.05027

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
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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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Yao D, Xu D, Lachapelle S, et al. Multi-view causal representation learning with partial observability. arXiv. doi:10.48550/arXiv.2311.04056
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arXiv: 2311.04056

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
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DOI: 10.48550/arXiv.2311.00664
arXiv: 2311.00664

Kori A, Locatello F, Ribeiro FDS, Toni F, Glocker B. Grounded object centric learning. arXiv. doi:10.48550/arXiv.2307.09437
LibreCat: https://research-explorer.ista.ac.at/record/14948
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arXiv: 2307.09437

Zhu Z, Locatello F, Cevher V. Sample complexity bounds for score-matching: Causal discovery and generative modeling. arXiv. doi:10.48550/arXiv.2310.18123
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arXiv: 2310.18123

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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arXiv: 2310.13387

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
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DOI: 10.48550/arXiv.2304.07939
arXiv: 2304.07939

Löwe S, Lippe P, Locatello F, Welling M. Rotating features for object discovery. arXiv. doi:10.48550/arXiv.2306.00600
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arXiv: 2306.00600

Zhao Z, Wang J, Horn M, et al. Object-centric multiple object tracking. arXiv. doi:10.48550/arXiv.2309.00233
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arXiv: 2309.00233

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
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arXiv: 2310.14246

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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arXiv: 2309.09858

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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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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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.
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arXiv: 2207.09239

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.
LibreCat: https://research-explorer.ista.ac.at/record/14106
arXiv: 2204.04440

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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arXiv: 2202.13212

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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arXiv: 2203.04913

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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arXiv: 2210.08031

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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arXiv: 2107.00637

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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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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arXiv: 2210.12733

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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arXiv: 2203.04413

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.
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arXiv: 2107.05686

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.
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arXiv: 2110.05304

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
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arXiv: 2201.13388

Rahaman N, Weiss M, Träuble F, et al. A general purpose neural architecture for geospatial systems. In: 36th Conference on Neural Information Processing Systems.
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arXiv: 2211.02348

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
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arXiv: 2210.01738

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.
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arXiv: 2006.07886

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.
LibreCat: https://research-explorer.ista.ac.at/record/14176
arXiv: 2106.05142

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.
LibreCat: https://research-explorer.ista.ac.at/record/14182
arXiv: 2107.01057

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
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arXiv: 2105.09240

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.
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arXiv: 2106.04619

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.
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arXiv: 2110.06399

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
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arXiv: 2102.11107

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.
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arXiv: 2010.14407

Locatello F. Enforcing and discovering structure in machine learning. arXiv. doi:10.48550/arXiv.2111.13693
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arXiv: 2111.13693

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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Stark SG, Ficek J, Locatello F, et al. SCIM: Universal single-cell matching with unpaired feature sets. Bioinformatics. 2020;36(Supplement_2):i919-i927. doi:10.1093/bioinformatics/btaa843
LibreCat: https://research-explorer.ista.ac.at/record/14125
DOI: 10.1093/bioinformatics/btaa843
PMID: 33381818

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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arXiv: 2007.14184

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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arXiv: 2002.02886

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.
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arXiv: 2002.11860

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.
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arXiv: 2010.14766

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.
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arXiv: 2006.15055

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: 1905.01258

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.
LibreCat: https://research-explorer.ista.ac.at/record/14189
arXiv: 1905.06642

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.
LibreCat: https://research-explorer.ista.ac.at/record/14197
arXiv: 1905.13662

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.
LibreCat: https://research-explorer.ista.ac.at/record/14191
arXiv: 1901.10348

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.
LibreCat: https://research-explorer.ista.ac.at/record/14193
arXiv: 1905.12506

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: 1811.12359

Gondal MW, Wüthrich M, Miladinovi , 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: 1906.03292

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: 1806.02185

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.
LibreCat: https://research-explorer.ista.ac.at/record/14201
arXiv: 1708.01733

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.
LibreCat: https://research-explorer.ista.ac.at/record/14198
arXiv: 1806.02199

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: 1804.08544

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.
LibreCat: https://research-explorer.ista.ac.at/record/14204
arXiv: 1803.09539

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: 1804.11130

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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DOI: 10.48550/arXiv.1804.11130
arXiv: 1804.11130

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: 1705.11041

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: 1702.06457