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J. Gamper, F. Kluibenschedl, A. K. H. Weiss, and T. S. Hofer, “Accessing position space wave functions in band structure calculations of periodic systems - a generalized, adapted numerov implementation for one-, two-, and three-dimensional quantum problems,” Journal of Physical Chemistry Letters, vol. 14, no. 33. American Chemical Society, pp. 7395–7403, 2023.
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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.
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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.
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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.
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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.
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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.
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M. Tangemann et al., “Unsupervised object learning via common fate,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 2023.
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M. Seitzer et al., “Bridging the gap to real-world object-centric learning,” in The 11th International Conference on Learning Representations, Kigali, Rwanda, 2023.
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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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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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D. R. Boocock, T. Hirashima, and E. B. Hannezo, “Interplay between mechanochemical patterning and glassy dynamics in cellular monolayers,” PRX Life, vol. 1, no. 1. American Physical Society, 2023.
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U. H. Cho and M. Hetzer, “Caspase-mediated nuclear pore complex trimming in cell differentiation and endoplasmic reticulum stress,” eLife, vol. 12. eLife Sciences Publications, 2023.
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M. Anastos, D. Fabian, A. Müyesser, and T. Szabó, “Splitting matchings and the Ryser-Brualdi-Stein conjecture for multisets,” Electronic Journal of Combinatorics, vol. 30, no. 3. Electronic Journal of Combinatorics, 2023.
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Y. Sun, H. Fu, K. Chatterjee, and A. K. Goharshady, “Automated tail bound analysis for probabilistic recurrence relations,” in Computer Aided Verification, Paris, France, 2023, vol. 13966, pp. 16–39.
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S. Akshay, K. Chatterjee, T. Meggendorfer, and D. Zikelic, “MDPs as distribution transformers: Affine invariant synthesis for safety objectives,” in International Conference on Computer Aided Verification, Paris, France, 2023, vol. 13966, pp. 86–112.
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M. Nagano, K. Aoshima, H. Shimamura, D. E. Siekhaus, J. Y. Toshima, and J. Toshima, “Distinct role of TGN-resident clathrin adaptors for Vpp activation in the TGN-endosome trafficking pathway,” Journal of Cell Science, vol. 136, no. 17. The Company of Biologists, 2023.
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P. M. Henderson, A. Ghazaryan, A. A. Zibrov, A. F. Young, and M. Serbyn, “Deep learning extraction of band structure parameters from density of states: A case study on trilayer graphene,” Physical Review B, vol. 108, no. 12. American Physical Society, 2023.
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