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102 Publications
2023 |
Published |
Conference Paper |
IST-REx-ID: 14219 |
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.
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| arXiv
2023 |
Published |
Conference Paper |
IST-REx-ID: 14222 |
M. Tangemann et al., “Unsupervised object learning via common fate,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 2023.
[Preprint]
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| arXiv
2023 |
Submitted |
Preprint |
IST-REx-ID: 14333 |
P. M. Faller, L. C. Vankadara, A. A. Mastakouri, F. Locatello, and D. Janzing, “Self-compatibility: Evaluating causal discovery without ground truth,” arXiv. .
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| arXiv
2023 |
Published |
Journal Article |
IST-REx-ID: 14949 |
M. Burg et al., “Image retrieval outperforms diffusion models on data augmentation,” Journal of Machine Learning Research. ML Research Press, 2023.
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2023 |
Published |
Conference Paper |
IST-REx-ID: 14958 |
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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2023 |
Submitted |
Preprint |
IST-REx-ID: 14961 |
F. Montagna, N. Noceti, L. Rosasco, and F. Locatello, “Shortcuts for causal discovery of nonlinear models by score matching,” arXiv. .
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| arXiv
2023 |
Submitted |
Preprint |
IST-REx-ID: 14962 |
K. Fan et al., “Unsupervised open-vocabulary object localization in videos,” arXiv. .
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| arXiv
2023 |
Submitted |
Preprint |
IST-REx-ID: 14963 |
Z. Zhao et al., “Object-centric multiple object tracking,” arXiv. .
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| arXiv
2023 |
Patent |
IST-REx-ID: 14965 |
J. Ficek, K.-V. Lehmann, F. Locatello, G. Raetsch, and S. Stark, “Methods of determining correspondences between biological properties of cells.” 2023.
[Published Version]
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2023 |
Submitted |
Conference Paper |
IST-REx-ID: 14974 |
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.
[Submitted Version]
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2023 |
Published |
Conference Paper |
IST-REx-ID: 14210 |
M. Fumero et al., “Leveraging sparse and shared feature activations for disentangled representation learning,” in 37th International Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2023, vol. 36.
[Published Version]
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| arXiv
2023 |
Published |
Conference Paper |
IST-REx-ID: 14952 |
V. Maiorca, L. Moschella, A. Norelli, M. Fumero, F. Locatello, and E. Rodolà, “Latent space translation via semantic alignment,” in 37th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2023, pp. 55394–55414.
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| DOI
| arXiv
2023 |
Published |
Conference Paper |
IST-REx-ID: 14207 |
S. Löwe, P. Lippe, F. Locatello, and M. Welling, “Rotating features for object discovery,” in 37th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2023, vol. 36.
[Published Version]
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| arXiv
2023 |
Published |
Conference Paper |
IST-REx-ID: 14948 |
A. Kori, F. Locatello, F. D. S. Ribeiro, F. Toni, and B. Glocker, “Grounded object centric learning,” in 12th International Conference on Learning Representations, Vienna, Austria, 2023.
[Published Version]
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| arXiv
2023 |
Published |
Conference Paper |
IST-REx-ID: 14954 |
F. Montagna et al., “Assumption violations in causal discovery and the robustness of score matching,” in 37th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2023, vol. 36.
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| arXiv
2023 |
Published |
Conference Paper |
IST-REx-ID: 14953 |
Z. Zhu, F. Locatello, and V. Cevher, “Sample complexity bounds for score-matching: Causal discovery and generative modeling,” in 37th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2023, vol. 36, pp. 3325–3337.
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| arXiv
2022 |
Published |
Conference Paper |
IST-REx-ID: 14093 |
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]
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| arXiv
2022 |
Published |
Conference Paper |
IST-REx-ID: 14106 |
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]
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| arXiv
2022 |
Published |
Conference Paper |
IST-REx-ID: 14107 |
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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| arXiv
2022 |
Published |
Conference Paper |
IST-REx-ID: 14114 |
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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| arXiv