Francesco Locatello

Locatello Group

71 Publications

Mark all

[71]
2024 | Conference Paper | IST-REx-ID: 14213 | OA
D. Lao, Z. Hu, F. Locatello, Y. Yang, and S. Soatto, “Divided attention: Unsupervised multi-object discovery with contextually separated slots,” in 1st Conference on Parsimony and Learning, Hong Kong, China, 2024.
[Published Version] View | Files available | arXiv
 
[70]
2023 | Conference Paper | IST-REx-ID: 14105 | OA
S. Sinha, P. Gehler, F. Locatello, and B. Schiele, “TeST: Test-time Self-Training under distribution shift,” in 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, United States, 2023.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[69]
2023 | Conference Paper | IST-REx-ID: 14208 | OA
Z. Zhu, F. Liu, G. G. Chrysos, F. Locatello, and V. Cevher, “Benign overfitting in deep neural networks under lazy training,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, United States, 2023, vol. 202, pp. 43105–43128.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[68]
2023 | Preprint | IST-REx-ID: 14209 | OA
M. F. Burg et al., “A data augmentation perspective on diffusion models and retrieval,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[67]
2023 | Conference Paper | IST-REx-ID: 14211 | OA
F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Causal discovery with score matching on additive models with arbitrary noise,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[66]
2023 | Conference Paper | IST-REx-ID: 14212 | OA
F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Scalable causal discovery with score matching,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[65]
2023 | Conference Paper | IST-REx-ID: 14214 | OA
Y. Liu et al., “Causal triplet: An open challenge for intervention-centric causal representation learning,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[64]
2023 | Conference Paper | IST-REx-ID: 14217 | OA
L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, and E. Rodolà, “Relative representations enable zero-shot latent space communication,” in The 11th International Conference on Learning Representations, Kigali, Rwanda, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[63]
2023 | Conference Paper | IST-REx-ID: 14222 | OA
M. Tangemann et al., “Unsupervised object learning via common fate,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[62]
2023 | Conference Paper | IST-REx-ID: 14218 | OA
M. Seitzer et al., “Bridging the gap to real-world object-centric learning,” in The 11th International Conference on Learning Representations, Kigali, Rwanda, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[61]
2023 | Conference Paper | IST-REx-ID: 14219 | OA
A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, and T. Brox, “Unsupervised semantic segmentation with self-supervised object-centric representations,” in The 11th International Conference on Learning Representations, Kigali, Rwanda, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[60]
2023 | Preprint | IST-REx-ID: 14333 | OA
P. M. Faller, L. C. Vankadara, A. A. Mastakouri, F. Locatello, and D. Janzing, “Self-compatibility: Evaluating causal discovery without ground truth,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[59]
2023 | Journal Article | IST-REx-ID: 14949 | OA
M. Burg et al., “Image retrieval outperforms diffusion models on data augmentation,” Journal of Machine Learning Research. ML Research Press, 2023.
[Published Version] View | Files available | Download Published Version (ext.)
 
[58]
2023 | Preprint | IST-REx-ID: 14946 | OA
D. Yao et al., “Multi-view causal representation learning with partial observability,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[57]
2023 | Preprint | IST-REx-ID: 14952 | OA
V. Maiorca, L. Moschella, A. Norelli, M. Fumero, F. Locatello, and E. Rodolà, “Latent space translation via semantic alignment,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[56]
2023 | Preprint | IST-REx-ID: 14948 | OA
A. Kori, F. Locatello, F. D. S. Ribeiro, F. Toni, and B. Glocker, “Grounded object centric learning,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[55]
2023 | Preprint | IST-REx-ID: 14953 | OA
Z. Zhu, F. Locatello, and V. Cevher, “Sample complexity bounds for score-matching: Causal discovery and generative modeling,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[54]
2023 | Preprint | IST-REx-ID: 14954 | OA
F. Montagna et al., “Assumption violations in causal discovery and the robustness of score matching,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[53]
2023 | Preprint | IST-REx-ID: 14210 | OA
M. Fumero et al., “Leveraging sparse and shared feature activations for disentangled representation learning,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[52]
2023 | Preprint | IST-REx-ID: 14207 | OA
S. Löwe, P. Lippe, F. Locatello, and M. Welling, “Rotating features for object discovery,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[51]
2023 | Preprint | IST-REx-ID: 14963 | OA
Z. Zhao et al., “Object-centric multiple object tracking,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[50]
2023 | Preprint | IST-REx-ID: 14961 | OA
F. Montagna, N. Noceti, L. Rosasco, and F. Locatello, “Shortcuts for causal discovery of nonlinear models by score matching,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[49]
2023 | Preprint | IST-REx-ID: 14962 | OA
K. Fan et al., “Unsupervised open-vocabulary object localization in videos,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[48]
2023 | Conference Paper | IST-REx-ID: 14958 | OA
D. Xu et al., “A sparsity principle for partially observable causal representation learning,” in Causal Representation Learning Workshop at NeurIPS 2023, New Orleans, LA, United States, 2023.
[Published Version] View | Files available | Download Published Version (ext.)
 
[47]
2023 | Conference Paper | IST-REx-ID: 14974 | OA
C. Zhang et al., “Causality in the time of LLMs: Round table discussion results of CLeaR 2023,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 2023.
[Submitted Version] View | Files available
 
[46]
2022 | Conference Paper | IST-REx-ID: 14173 | OA
F. Wenzel et al., “Assaying out-of-distribution generalization in transfer learning,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022, vol. 35, pp. 7181–7198.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[45]
2022 | Conference Paper | IST-REx-ID: 14106 | OA
M. Lohaus, M. Kleindessner, K. Kenthapadi, F. Locatello, and C. Russell, “Are two heads the same as one? Identifying disparate treatment in fair neural networks,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022, vol. 35, pp. 16548–16562.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[44]
2022 | Conference Paper | IST-REx-ID: 14093 | OA
G. Dresdner, M.-L. Vladarean, G. Rätsch, F. Locatello, V. Cevher, and A. Yurtsever, “ Faster one-sample stochastic conditional gradient method for composite convex minimization,” in Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, Virtual, 2022, vol. 151, pp. 8439–8457.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[43]
2022 | Conference Paper | IST-REx-ID: 14114 | OA
D. Zietlow et al., “Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, United States, 2022, pp. 10400–10411.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[42]
2022 | Conference Paper | IST-REx-ID: 14168 | OA
N. Rahaman et al., “Neural attentive circuits,” in 36th Conference on Neural Information Processing Systems, New Orleans, United States, 2022, vol. 35.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[41]
2022 | Conference Paper | IST-REx-ID: 14170 | OA
A. Dittadi, S. Papa, M. D. Vita, B. Schölkopf, O. Winther, and F. Locatello, “Generalization and robustness implications in object-centric learning,” in Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, United States, vol. 2022, pp. 5221–5285.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[40]
2022 | Conference Paper | IST-REx-ID: 14172 | OA
L. Schott et al., “Visual representation learning does not generalize strongly within the  same domain,” in 10th International Conference on Learning Representations, Virtual, 2022.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[39]
2022 | Conference Paper | IST-REx-ID: 14107 | OA
J. Yao et al., “Self-supervised amodal video object segmentation,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[38]
2022 | Conference Paper | IST-REx-ID: 14171 | OA
P. Rolland et al., “Score matching enables causal discovery of nonlinear additive noise  models,” in Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, United States, 2022, vol. 162, pp. 18741–18753.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[37]
2022 | Conference Paper | IST-REx-ID: 14174 | OA
A. Dittadi et al., “The role of pretrained representations for the OOD generalization of  reinforcement learning agents,” in 10th International Conference on Learning Representations, Virtual, 2022.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[36]
2022 | Conference Paper | IST-REx-ID: 14175 | OA
O. Makansi et al., “You mostly walk alone: Analyzing feature attribution in trajectory prediction,” in 10th International Conference on Learning Representations, Virtual, 2022.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[35]
2022 | Preprint | IST-REx-ID: 14220 | OA
D. Mambelli, F. Träuble, S. Bauer, B. Schölkopf, and F. Locatello, “Compositional multi-object reinforcement learning with linear relation networks,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[34]
2022 | Conference Paper | IST-REx-ID: 14215 | OA
N. Rahaman et al., “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
 
[33]
2022 | Preprint | IST-REx-ID: 14216 | OA
A. Norelli, M. Fumero, V. Maiorca, L. Moschella, E. Rodolà, and F. Locatello, “ASIF: Coupled data turns unimodal models to multimodal without training,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[32]
2021 | Conference Paper | IST-REx-ID: 14177 | OA
F. Träuble et al., “On disentangled representations learned from correlated data,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 10401–10412.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[31]
2021 | Conference Paper | IST-REx-ID: 14176 | OA
H. Yèche, G. Dresdner, F. Locatello, M. Hüser, and G. Rätsch, “Neighborhood contrastive learning applied to online patient monitoring,” in Proceedings of 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 11964–11974.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[30]
2021 | Conference Paper | IST-REx-ID: 14182 | OA
F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, and P. Gehler, “Backward-compatible prediction updates: A probabilistic approach,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 116–128.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[29]
2021 | Conference Paper | IST-REx-ID: 14181 | OA
G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, and G. Rätsch, “Boosting variational inference with locally adaptive step-sizes,” in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Montreal, Canada, 2021, pp. 2337–2343.
[Published Version] View | DOI | Download Published Version (ext.) | arXiv
 
[28]
2021 | Conference Paper | IST-REx-ID: 14179 | OA
J. von Kügelgen et al., “Self-supervised learning with data augmentations provably isolates content from style,” in Advances in Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 16451–16467.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[27]
2021 | Conference Paper | IST-REx-ID: 14180 | OA
N. Rahaman et al., “Dynamic inference with neural interpreters,” in Advances in Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 10985–10998.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[26]
2021 | Journal Article | IST-REx-ID: 14117 | OA
B. Scholkopf et al., “Toward causal representation learning,” Proceedings of the IEEE, vol. 109, no. 5. Institute of Electrical and Electronics Engineers, pp. 612–634, 2021.
[Published Version] View | DOI | Download Published Version (ext.) | arXiv
 
[25]
2021 | Conference Paper | IST-REx-ID: 14178 | OA
A. Dittadi et al., “On the transfer of disentangled representations in realistic settings,” in The Ninth International Conference on Learning Representations, Virtual, 2021.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[24]
2021 | Preprint | IST-REx-ID: 14221 | OA
F. Locatello, “Enforcing and discovering structure in machine learning,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[23]
2021 | Conference Paper | IST-REx-ID: 14332
F. Träuble et al., “Representation learning for out-of-distribution generalization in reinforcement learning,” in ICML 2021 Workshop on Unsupervised Reinforcement Learning, Virtual, 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
F. Locatello et al., “A commentary on the unsupervised learning of disentangled representations,” in The 34th AAAI Conference on Artificial Intelligence, New York, NY, United States, 2020, vol. 34, no. 9, pp. 13681–13684.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[20]
2020 | Conference Paper | IST-REx-ID: 14188 | OA
F. Locatello, B. Poole, G. Rätsch, B. Schölkopf, O. Bachem, and M. Tschannen, “Weakly-supervised disentanglement without compromises,” in Proceedings of the 37th International Conference on Machine Learning, Virtual, 2020, vol. 119, pp. 6348–6359.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[19]
2020 | Conference Paper | IST-REx-ID: 14187 | OA
G. Négiar et al., “Stochastic Frank-Wolfe for constrained finite-sum minimization,” in Proceedings of the 37th International Conference on Machine Learning, Virtual, 2020, vol. 119, pp. 7253–7262.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[18]
2020 | Journal Article | IST-REx-ID: 14195 | OA
F. Locatello et al., “A sober look at the unsupervised learning of disentangled representations and their evaluation,” Journal of Machine Learning Research, vol. 21. MIT Press, 2020.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[17]
2020 | Conference Paper | IST-REx-ID: 14326 | OA
F. Locatello et al., “Object-centric learning with slot attention,” in Advances in Neural Information Processing Systems, Virtual, 2020, vol. 33, pp. 11525–11538.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[16]
2019 | Conference Paper | IST-REx-ID: 14184 | OA
F. Locatello, M. Tschannen, S. Bauer, G. Rätsch, B. Schölkopf, and O. Bachem, “Disentangling factors of variation using few labels,” in 8th International Conference on Learning Representations, Virtual, 2019.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[15]
2019 | Conference Paper | IST-REx-ID: 14189 | OA
L. Gresele, P. K. Rubenstein, A. Mehrjou, F. Locatello, and B. Schölkopf, “The incomplete Rosetta Stone problem: Identifiability results for multi-view nonlinear ICA,” in Proceedings of the 35th Conference on Uncertainty in Artificial  Intelligence, Tel Aviv, Israel, 2019, vol. 115, pp. 217–227.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[14]
2019 | Conference Paper | IST-REx-ID: 14197 | OA
F. Locatello, G. Abbati, T. Rainforth, S. Bauer, B. Schölkopf, and O. Bachem, “On the fairness of disentangled representations,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32, pp. 14611–14624.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[13]
2019 | Conference Paper | IST-REx-ID: 14191 | OA
F. Locatello, A. Yurtsever, O. Fercoq, and V. Cevher, “Stochastic Frank-Wolfe for composite convex minimization,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32, pp. 14291–14301.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[12]
2019 | Conference Paper | IST-REx-ID: 14193 | OA
S. van Steenkiste, F. Locatello, J. Schmidhuber, and O. Bachem, “Are disentangled representations helpful for abstract visual reasoning?,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[11]
2019 | Conference Paper | IST-REx-ID: 14200 | OA
F. Locatello et al., “Challenging common assumptions in the unsupervised learning of disentangled representations,” in Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, United States, 2019, vol. 97, pp. 4114–4124.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[10]
2019 | Conference Paper | IST-REx-ID: 14190 | OA
M. W. Gondal 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, Vancouver, Canada, 2019, vol. 32.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[9]
2018 | Conference Paper | IST-REx-ID: 14202 | OA
F. Locatello, G. Dresdner, R. Khanna, I. Valera, and G. Rätsch, “Boosting black box variational inference,” in Advances in Neural Information Processing Systems, Montreal, Canada, 2018, vol. 31.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[8]
2018 | Conference Paper | IST-REx-ID: 14201 | OA
F. Locatello, R. Khanna, J. Ghosh, and G. Rätsch, “Boosting variational inference: An optimization perspective,” in Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, Playa Blanca, Lanzarote, 2018, vol. 84, pp. 464–472.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[7]
2018 | Conference Paper | IST-REx-ID: 14198 | OA
V. Fortuin, M. Hüser, F. Locatello, H. Strathmann, and G. Rätsch, “SOM-VAE: Interpretable discrete representation learning on time series,” in International Conference on Learning Representations, New Orleans, LA, United States, 2018.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[6]
2018 | Conference Paper | IST-REx-ID: 14203 | OA
A. Yurtsever, O. Fercoq, F. Locatello, and V. Cevher, “A conditional gradient framework for composite convex minimization with applications to semidefinite programming,” in Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, 2018, vol. 80, pp. 5727–5736.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[5]
2018 | Conference Paper | IST-REx-ID: 14204 | OA
F. Locatello et al., “On matching pursuit and coordinate descent,” in Proceedings of the 35th International Conference on Machine Learning, 2018, vol. 80, pp. 3198–3207.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[4]
2018 | Conference Paper | IST-REx-ID: 14224 | OA
F. Locatello, D. Vincent, I. Tolstikhin, G. Ratsch, S. Gelly, and B. Scholkopf, “Clustering meets implicit generative models,” in 6th International Conference on Learning Representations, Vancouver, Canada, 2018.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[3]
2018 | Preprint | IST-REx-ID: 14327 | OA
F. Locatello, D. Vincent, I. Tolstikhin, G. Rätsch, S. Gelly, and B. Schölkopf, “Competitive training of mixtures of independent deep generative models,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[2]
2017 | Conference Paper | IST-REx-ID: 14206 | OA
F. Locatello, M. Tschannen, G. Rätsch, and M. Jaggi, “Greedy algorithms for cone constrained optimization with convergence guarantees,” in Advances in Neural Information Processing Systems, Long Beach, CA, United States, 2017.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[1]
2017 | Conference Paper | IST-REx-ID: 14205 | OA
F. Locatello, R. Khanna, M. Tschannen, and M. Jaggi, “A unified optimization view on generalized matching pursuit and Frank-Wolfe,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, United States, 2017, vol. 54, pp. 860–868.
[Preprint] View | Download Preprint (ext.) | arXiv
 
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71 Publications

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[71]
2024 | Conference Paper | IST-REx-ID: 14213 | OA
D. Lao, Z. Hu, F. Locatello, Y. Yang, and S. Soatto, “Divided attention: Unsupervised multi-object discovery with contextually separated slots,” in 1st Conference on Parsimony and Learning, Hong Kong, China, 2024.
[Published Version] View | Files available | arXiv
 
[70]
2023 | Conference Paper | IST-REx-ID: 14105 | OA
S. Sinha, P. Gehler, F. Locatello, and B. Schiele, “TeST: Test-time Self-Training under distribution shift,” in 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, United States, 2023.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[69]
2023 | Conference Paper | IST-REx-ID: 14208 | OA
Z. Zhu, F. Liu, G. G. Chrysos, F. Locatello, and V. Cevher, “Benign overfitting in deep neural networks under lazy training,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, United States, 2023, vol. 202, pp. 43105–43128.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[68]
2023 | Preprint | IST-REx-ID: 14209 | OA
M. F. Burg et al., “A data augmentation perspective on diffusion models and retrieval,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[67]
2023 | Conference Paper | IST-REx-ID: 14211 | OA
F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Causal discovery with score matching on additive models with arbitrary noise,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[66]
2023 | Conference Paper | IST-REx-ID: 14212 | OA
F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Scalable causal discovery with score matching,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[65]
2023 | Conference Paper | IST-REx-ID: 14214 | OA
Y. Liu et al., “Causal triplet: An open challenge for intervention-centric causal representation learning,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[64]
2023 | Conference Paper | IST-REx-ID: 14217 | OA
L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, and E. Rodolà, “Relative representations enable zero-shot latent space communication,” in The 11th International Conference on Learning Representations, Kigali, Rwanda, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[63]
2023 | Conference Paper | IST-REx-ID: 14222 | OA
M. Tangemann et al., “Unsupervised object learning via common fate,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[62]
2023 | Conference Paper | IST-REx-ID: 14218 | OA
M. Seitzer et al., “Bridging the gap to real-world object-centric learning,” in The 11th International Conference on Learning Representations, Kigali, Rwanda, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[61]
2023 | Conference Paper | IST-REx-ID: 14219 | OA
A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, and T. Brox, “Unsupervised semantic segmentation with self-supervised object-centric representations,” in The 11th International Conference on Learning Representations, Kigali, Rwanda, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[60]
2023 | Preprint | IST-REx-ID: 14333 | OA
P. M. Faller, L. C. Vankadara, A. A. Mastakouri, F. Locatello, and D. Janzing, “Self-compatibility: Evaluating causal discovery without ground truth,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[59]
2023 | Journal Article | IST-REx-ID: 14949 | OA
M. Burg et al., “Image retrieval outperforms diffusion models on data augmentation,” Journal of Machine Learning Research. ML Research Press, 2023.
[Published Version] View | Files available | Download Published Version (ext.)
 
[58]
2023 | Preprint | IST-REx-ID: 14946 | OA
D. Yao et al., “Multi-view causal representation learning with partial observability,” arXiv. .
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[57]
2023 | Preprint | IST-REx-ID: 14952 | OA
V. Maiorca, L. Moschella, A. Norelli, M. Fumero, F. Locatello, and E. Rodolà, “Latent space translation via semantic alignment,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[56]
2023 | Preprint | IST-REx-ID: 14948 | OA
A. Kori, F. Locatello, F. D. S. Ribeiro, F. Toni, and B. Glocker, “Grounded object centric learning,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[55]
2023 | Preprint | IST-REx-ID: 14953 | OA
Z. Zhu, F. Locatello, and V. Cevher, “Sample complexity bounds for score-matching: Causal discovery and generative modeling,” arXiv. .
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[54]
2023 | Preprint | IST-REx-ID: 14954 | OA
F. Montagna et al., “Assumption violations in causal discovery and the robustness of score matching,” arXiv. .
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[53]
2023 | Preprint | IST-REx-ID: 14210 | OA
M. Fumero et al., “Leveraging sparse and shared feature activations for disentangled representation learning,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[52]
2023 | Preprint | IST-REx-ID: 14207 | OA
S. Löwe, P. Lippe, F. Locatello, and M. Welling, “Rotating features for object discovery,” arXiv. .
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[51]
2023 | Preprint | IST-REx-ID: 14963 | OA
Z. Zhao et al., “Object-centric multiple object tracking,” arXiv. .
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[50]
2023 | Preprint | IST-REx-ID: 14961 | OA
F. Montagna, N. Noceti, L. Rosasco, and F. Locatello, “Shortcuts for causal discovery of nonlinear models by score matching,” arXiv. .
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[49]
2023 | Preprint | IST-REx-ID: 14962 | OA
K. Fan et al., “Unsupervised open-vocabulary object localization in videos,” arXiv. .
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[48]
2023 | Conference Paper | IST-REx-ID: 14958 | OA
D. Xu et al., “A sparsity principle for partially observable causal representation learning,” in Causal Representation Learning Workshop at NeurIPS 2023, New Orleans, LA, United States, 2023.
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[47]
2023 | Conference Paper | IST-REx-ID: 14974 | OA
C. Zhang et al., “Causality in the time of LLMs: Round table discussion results of CLeaR 2023,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 2023.
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[46]
2022 | Conference Paper | IST-REx-ID: 14173 | OA
F. Wenzel et al., “Assaying out-of-distribution generalization in transfer learning,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022, vol. 35, pp. 7181–7198.
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[45]
2022 | Conference Paper | IST-REx-ID: 14106 | OA
M. Lohaus, M. Kleindessner, K. Kenthapadi, F. Locatello, and C. Russell, “Are two heads the same as one? Identifying disparate treatment in fair neural networks,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022, vol. 35, pp. 16548–16562.
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[44]
2022 | Conference Paper | IST-REx-ID: 14093 | OA
G. Dresdner, M.-L. Vladarean, G. Rätsch, F. Locatello, V. Cevher, and A. Yurtsever, “ Faster one-sample stochastic conditional gradient method for composite convex minimization,” in Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, Virtual, 2022, vol. 151, pp. 8439–8457.
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[43]
2022 | Conference Paper | IST-REx-ID: 14114 | OA
D. Zietlow et al., “Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, United States, 2022, pp. 10400–10411.
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[42]
2022 | Conference Paper | IST-REx-ID: 14168 | OA
N. Rahaman et al., “Neural attentive circuits,” in 36th Conference on Neural Information Processing Systems, New Orleans, United States, 2022, vol. 35.
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[41]
2022 | Conference Paper | IST-REx-ID: 14170 | OA
A. Dittadi, S. Papa, M. D. Vita, B. Schölkopf, O. Winther, and F. Locatello, “Generalization and robustness implications in object-centric learning,” in Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, United States, vol. 2022, pp. 5221–5285.
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[40]
2022 | Conference Paper | IST-REx-ID: 14172 | OA
L. Schott et al., “Visual representation learning does not generalize strongly within the  same domain,” in 10th International Conference on Learning Representations, Virtual, 2022.
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[39]
2022 | Conference Paper | IST-REx-ID: 14107 | OA
J. Yao et al., “Self-supervised amodal video object segmentation,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022.
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[38]
2022 | Conference Paper | IST-REx-ID: 14171 | OA
P. Rolland et al., “Score matching enables causal discovery of nonlinear additive noise  models,” in Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, United States, 2022, vol. 162, pp. 18741–18753.
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[37]
2022 | Conference Paper | IST-REx-ID: 14174 | OA
A. Dittadi et al., “The role of pretrained representations for the OOD generalization of  reinforcement learning agents,” in 10th International Conference on Learning Representations, Virtual, 2022.
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[36]
2022 | Conference Paper | IST-REx-ID: 14175 | OA
O. Makansi et al., “You mostly walk alone: Analyzing feature attribution in trajectory prediction,” in 10th International Conference on Learning Representations, Virtual, 2022.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[35]
2022 | Preprint | IST-REx-ID: 14220 | OA
D. Mambelli, F. Träuble, S. Bauer, B. Schölkopf, and F. Locatello, “Compositional multi-object reinforcement learning with linear relation networks,” arXiv. .
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[34]
2022 | Conference Paper | IST-REx-ID: 14215 | OA
N. Rahaman et al., “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
 
[33]
2022 | Preprint | IST-REx-ID: 14216 | OA
A. Norelli, M. Fumero, V. Maiorca, L. Moschella, E. Rodolà, and F. Locatello, “ASIF: Coupled data turns unimodal models to multimodal without training,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[32]
2021 | Conference Paper | IST-REx-ID: 14177 | OA
F. Träuble et al., “On disentangled representations learned from correlated data,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 10401–10412.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[31]
2021 | Conference Paper | IST-REx-ID: 14176 | OA
H. Yèche, G. Dresdner, F. Locatello, M. Hüser, and G. Rätsch, “Neighborhood contrastive learning applied to online patient monitoring,” in Proceedings of 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 11964–11974.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[30]
2021 | Conference Paper | IST-REx-ID: 14182 | OA
F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, and P. Gehler, “Backward-compatible prediction updates: A probabilistic approach,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 116–128.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[29]
2021 | Conference Paper | IST-REx-ID: 14181 | OA
G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, and G. Rätsch, “Boosting variational inference with locally adaptive step-sizes,” in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Montreal, Canada, 2021, pp. 2337–2343.
[Published Version] View | DOI | Download Published Version (ext.) | arXiv
 
[28]
2021 | Conference Paper | IST-REx-ID: 14179 | OA
J. von Kügelgen et al., “Self-supervised learning with data augmentations provably isolates content from style,” in Advances in Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 16451–16467.
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[27]
2021 | Conference Paper | IST-REx-ID: 14180 | OA
N. Rahaman et al., “Dynamic inference with neural interpreters,” in Advances in Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 10985–10998.
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[26]
2021 | Journal Article | IST-REx-ID: 14117 | OA
B. Scholkopf et al., “Toward causal representation learning,” Proceedings of the IEEE, vol. 109, no. 5. Institute of Electrical and Electronics Engineers, pp. 612–634, 2021.
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[25]
2021 | Conference Paper | IST-REx-ID: 14178 | OA
A. Dittadi et al., “On the transfer of disentangled representations in realistic settings,” in The Ninth International Conference on Learning Representations, Virtual, 2021.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[24]
2021 | Preprint | IST-REx-ID: 14221 | OA
F. Locatello, “Enforcing and discovering structure in machine learning,” arXiv. .
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[23]
2021 | Conference Paper | IST-REx-ID: 14332
F. Träuble et al., “Representation learning for out-of-distribution generalization in reinforcement learning,” in ICML 2021 Workshop on Unsupervised Reinforcement Learning, Virtual, 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
F. Locatello et al., “A commentary on the unsupervised learning of disentangled representations,” in The 34th AAAI Conference on Artificial Intelligence, New York, NY, United States, 2020, vol. 34, no. 9, pp. 13681–13684.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[20]
2020 | Conference Paper | IST-REx-ID: 14188 | OA
F. Locatello, B. Poole, G. Rätsch, B. Schölkopf, O. Bachem, and M. Tschannen, “Weakly-supervised disentanglement without compromises,” in Proceedings of the 37th International Conference on Machine Learning, Virtual, 2020, vol. 119, pp. 6348–6359.
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[19]
2020 | Conference Paper | IST-REx-ID: 14187 | OA
G. Négiar et al., “Stochastic Frank-Wolfe for constrained finite-sum minimization,” in Proceedings of the 37th International Conference on Machine Learning, Virtual, 2020, vol. 119, pp. 7253–7262.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[18]
2020 | Journal Article | IST-REx-ID: 14195 | OA
F. Locatello et al., “A sober look at the unsupervised learning of disentangled representations and their evaluation,” Journal of Machine Learning Research, vol. 21. MIT Press, 2020.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[17]
2020 | Conference Paper | IST-REx-ID: 14326 | OA
F. Locatello et al., “Object-centric learning with slot attention,” in Advances in Neural Information Processing Systems, Virtual, 2020, vol. 33, pp. 11525–11538.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[16]
2019 | Conference Paper | IST-REx-ID: 14184 | OA
F. Locatello, M. Tschannen, S. Bauer, G. Rätsch, B. Schölkopf, and O. Bachem, “Disentangling factors of variation using few labels,” in 8th International Conference on Learning Representations, Virtual, 2019.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[15]
2019 | Conference Paper | IST-REx-ID: 14189 | OA
L. Gresele, P. K. Rubenstein, A. Mehrjou, F. Locatello, and B. Schölkopf, “The incomplete Rosetta Stone problem: Identifiability results for multi-view nonlinear ICA,” in Proceedings of the 35th Conference on Uncertainty in Artificial  Intelligence, Tel Aviv, Israel, 2019, vol. 115, pp. 217–227.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[14]
2019 | Conference Paper | IST-REx-ID: 14197 | OA
F. Locatello, G. Abbati, T. Rainforth, S. Bauer, B. Schölkopf, and O. Bachem, “On the fairness of disentangled representations,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32, pp. 14611–14624.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[13]
2019 | Conference Paper | IST-REx-ID: 14191 | OA
F. Locatello, A. Yurtsever, O. Fercoq, and V. Cevher, “Stochastic Frank-Wolfe for composite convex minimization,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32, pp. 14291–14301.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[12]
2019 | Conference Paper | IST-REx-ID: 14193 | OA
S. van Steenkiste, F. Locatello, J. Schmidhuber, and O. Bachem, “Are disentangled representations helpful for abstract visual reasoning?,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[11]
2019 | Conference Paper | IST-REx-ID: 14200 | OA
F. Locatello et al., “Challenging common assumptions in the unsupervised learning of disentangled representations,” in Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, United States, 2019, vol. 97, pp. 4114–4124.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[10]
2019 | Conference Paper | IST-REx-ID: 14190 | OA
M. W. Gondal 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, Vancouver, Canada, 2019, vol. 32.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[9]
2018 | Conference Paper | IST-REx-ID: 14202 | OA
F. Locatello, G. Dresdner, R. Khanna, I. Valera, and G. Rätsch, “Boosting black box variational inference,” in Advances in Neural Information Processing Systems, Montreal, Canada, 2018, vol. 31.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[8]
2018 | Conference Paper | IST-REx-ID: 14201 | OA
F. Locatello, R. Khanna, J. Ghosh, and G. Rätsch, “Boosting variational inference: An optimization perspective,” in Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, Playa Blanca, Lanzarote, 2018, vol. 84, pp. 464–472.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[7]
2018 | Conference Paper | IST-REx-ID: 14198 | OA
V. Fortuin, M. Hüser, F. Locatello, H. Strathmann, and G. Rätsch, “SOM-VAE: Interpretable discrete representation learning on time series,” in International Conference on Learning Representations, New Orleans, LA, United States, 2018.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[6]
2018 | Conference Paper | IST-REx-ID: 14203 | OA
A. Yurtsever, O. Fercoq, F. Locatello, and V. Cevher, “A conditional gradient framework for composite convex minimization with applications to semidefinite programming,” in Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, 2018, vol. 80, pp. 5727–5736.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[5]
2018 | Conference Paper | IST-REx-ID: 14204 | OA
F. Locatello et al., “On matching pursuit and coordinate descent,” in Proceedings of the 35th International Conference on Machine Learning, 2018, vol. 80, pp. 3198–3207.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[4]
2018 | Conference Paper | IST-REx-ID: 14224 | OA
F. Locatello, D. Vincent, I. Tolstikhin, G. Ratsch, S. Gelly, and B. Scholkopf, “Clustering meets implicit generative models,” in 6th International Conference on Learning Representations, Vancouver, Canada, 2018.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[3]
2018 | Preprint | IST-REx-ID: 14327 | OA
F. Locatello, D. Vincent, I. Tolstikhin, G. Rätsch, S. Gelly, and B. Schölkopf, “Competitive training of mixtures of independent deep generative models,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[2]
2017 | Conference Paper | IST-REx-ID: 14206 | OA
F. Locatello, M. Tschannen, G. Rätsch, and M. Jaggi, “Greedy algorithms for cone constrained optimization with convergence guarantees,” in Advances in Neural Information Processing Systems, Long Beach, CA, United States, 2017.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[1]
2017 | Conference Paper | IST-REx-ID: 14205 | OA
F. Locatello, R. Khanna, M. Tschannen, and M. Jaggi, “A unified optimization view on generalized matching pursuit and Frank-Wolfe,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, United States, 2017, vol. 54, pp. 860–868.
[Preprint] View | Download Preprint (ext.) | arXiv
 
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