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5388 Publications
2023 | Published | Conference Abstract | IST-REx-ID: 14866 | 
S. Abramian, C. J. Muller, and C. Risi, “Extreme precipitation in tropical squall lines,” in EGU General Assembly 2023, Vienna, Austria & Virtual, 2023.
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2023 | Published | Conference Paper | IST-REx-ID: 14867 |
M. Anastos, “Constructing Hamilton cycles and perfect matchings efficiently,” in Proceedings of the 12th European Conference on Combinatorics, Graph Theory and Applications, Prague, Czech Republic, 2023, pp. 36–41.
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| arXiv
2023 | Epub ahead of print | Journal Article | IST-REx-ID: 14868 |
S. Tyagi et al., “High-precision mapping of nuclear pore-chromatin interactions reveals new principles of genome organization at the nuclear envelope,” eLife. eLife Sciences Publications, 2023.
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2023 | Research Data Reference | IST-REx-ID: 14892 |
D. Feitosa Tomé, “douglastome/dynamic-engrams: Dynamic and selective engrams emerge with memory consolidation.” Zenodo, 2023.
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2023 | Research Data Reference | IST-REx-ID: 14919 |
T. Shaw, P. Buri, M. McCarthy, E. Miles, and F. Pellicciotti, “Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer.” Zenodo, 2023.
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2023 | Published | Journal Article | IST-REx-ID: 14920 |
T. Banerjee, R. Majumdar, K. Mallik, A.-K. Schmuck, and S. Soudjani, “Fast symbolic algorithms for mega-regular games under strong transition fairness,” TheoretiCS, vol. 2. EPI Sciences, 2023.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14921 |
P. Súkeník, M. Mondelli, and C. Lampert, “Deep neural collapse is provably optimal for the deep unconstrained features model,” in 37th Annual Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2023.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14923 |
T. Fu, Y. Liu, J. Barbier, M. Mondelli, S. Liang, and T. Hou, “Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise,” in Proceedings of 2023 IEEE International Symposium on Information Theory, Taipei, Taiwan, 2023, pp. 1178–1183.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14924 |
D. Wu, V. Kungurtsev, and M. Mondelli, “Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence,” in Transactions on Machine Learning Research, 2023.
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| arXiv
2023 | Submitted | Preprint | IST-REx-ID: 14946 |
D. Yao et al., “Multi-view causal representation learning with partial observability,” arXiv. .
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| arXiv
2023 | Submitted | Preprint | IST-REx-ID: 14948 |
A. Kori, F. Locatello, F. D. S. Ribeiro, F. Toni, and B. Glocker, “Grounded object centric learning,” 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 | Submitted | Preprint | IST-REx-ID: 14952 |
V. Maiorca, L. Moschella, A. Norelli, M. Fumero, F. Locatello, and E. Rodolà, “Latent space translation via semantic alignment,” arXiv. .
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| arXiv
2023 | Submitted | Preprint | IST-REx-ID: 14953 |
Z. Zhu, F. Locatello, and V. Cevher, “Sample complexity bounds for score-matching: Causal discovery and generative modeling,” arXiv. .
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| arXiv
2023 | Submitted | Preprint | IST-REx-ID: 14954 |
F. Montagna et al., “Assumption violations in causal discovery and the robustness of score matching,” arXiv. .
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| arXiv
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