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90 Publications
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2023 | Patent | IST-REx-ID: 14965 | 
Ficek J, Lehmann K-V, Locatello F, Raetsch G, Stark S. Methods of determining correspondences between biological properties of cells. 2023.
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2023 | Published | Conference Paper | IST-REx-ID: 14958 |
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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2023 | Published | Conference Paper | IST-REx-ID: 14216 |
Norelli A, Fumero M, Maiorca V, Moschella L, Rodolà E, Locatello F. ASIF: Coupled data turns unimodal models to multimodal without training. In: 37th Conference on Neural Information Processing Systems. Vol 36. Neural Information Processing Systems Foundation; 2023:15303-15319.
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| arXiv
2023 | Submitted | Conference Paper | IST-REx-ID: 14974 |
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.
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2022 | Published | Conference Paper | IST-REx-ID: 14093 |
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
2022 | Published | Conference Paper | IST-REx-ID: 14106 |
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.
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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 14107 |
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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2022 | Published | Conference Paper | IST-REx-ID: 14114 |
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
2022 | Published | Conference Paper | IST-REx-ID: 14168 |
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
2022 | Submitted | Conference Paper | IST-REx-ID: 14170 |
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
2022 | Published | Conference Paper | IST-REx-ID: 14171 |
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
2022 | Published | Conference Paper | IST-REx-ID: 14172 |
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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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 14173 |
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
2022 | Published | Conference Paper | IST-REx-ID: 14174 |
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
2022 | Published | Conference Paper | IST-REx-ID: 14175 |
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
2022 | Submitted | Conference Paper | IST-REx-ID: 14215 |
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
2022 | Submitted | Preprint | IST-REx-ID: 14220 |
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
2021 | Published | Journal Article | IST-REx-ID: 14117 |
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
2021 | Published | Conference Paper | IST-REx-ID: 14176 |
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.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 14177 |
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
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