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arXiv: 2305.19377

Burg, M. F., Wenzel, F., Zietlow, D., Horn, M., Makansi, O., Locatello, F., &; Russell, C. (n.d.). A data augmentation perspective on diffusion models and retrieval. arXiv.https://doi.org/10.48550/arXiv.2304.10253 
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arXiv: 2304.10253

Montagna, F., Noceti, N., Rosasco, L., Zhang, K., &; Locatello, F. (2023). Causal discovery with score matching on additive models with arbitrary noise. In 2nd Conference on Causal Learning and Reasoning. Tübingen, Germany.
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arXiv: 2304.03265

Montagna, F., Noceti, N., Rosasco, L., Zhang, K., &; Locatello, F. (2023). Scalable causal discovery with score matching. In 2nd Conference on Causal Learning and Reasoning. Tübingen, Germany.
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arXiv: 2304.03382

Liu, Y., Alahi, A., Russell, C., Horn, M., Zietlow, D., Schölkopf, B., &; Locatello, F. (2023). Causal triplet: An open challenge for intervention-centric causal representation learning. In 2nd Conference on Causal Learning and Reasoning. Tübingen, Germany.
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arXiv: 2301.05169

Moschella, L., Maiorca, V., Fumero, M., Norelli, A., Locatello, F., &; Rodolà, E. (2023). Relative representations enable zero-shot latent space communication. In The 11th International Conference on Learning Representations. Kigali, Rwanda.
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arXiv: 2209.15430

Tangemann, M., Schneider, S., Kügelgen, J. von, Locatello, F., Gehler, P., Brox, T.,  Schölkopf, B. (2023). Unsupervised object learning via common fate. In 2nd Conference on Causal Learning and Reasoning. Tübingen, Germany.
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arXiv: 2110.06562

Seitzer, M., Horn, M., Zadaianchuk, A., Zietlow, D., Xiao, T., Carl-Johann Simon-Gabriel, C.-J. S.-G.,  Locatello, F. (2023). Bridging the gap to real-world object-centric learning. In The 11th International Conference on Learning Representations. Kigali, Rwanda.
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arXiv: 2209.14860

Zadaianchuk, A., Kleindessner, M., Zhu, Y., Locatello, F., &; Brox, T. (2023). Unsupervised semantic segmentation with self-supervised object-centric representations. In The 11th International Conference on Learning Representations. Kigali, Rwanda.
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arXiv: 2207.05027

Faller, P. M., Vankadara, L. C., Mastakouri, A. A., Locatello, F., &; Janzing, D. (n.d.). Self-compatibility: Evaluating causal discovery without ground truth. arXiv.https://doi.org/10.48550/arXiv.2307.09552 
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Boocock, D. R., Hirashima, T., &; Hannezo, E. B. (2023). Interplay between mechanochemical patterning and glassy dynamics in cellular monolayers. PRX Life. American Physical Society.https://doi.org/10.1103/prxlife.1.013001 
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Nardin, M., Käfer, K., Stella, F., &; Csicsvari, J. L. (2023). Theta oscillations as a substrate for medial prefrontal-hippocampal assembly interactions. Cell Reports. Elsevier.https://doi.org/10.1016/j.celrep.2023.113015 
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Cho, U. H., &; Hetzer, M. (2023). Caspase-mediated nuclear pore complex trimming in cell differentiation and endoplasmic reticulum stress. ELife. eLife Sciences Publications.https://doi.org/10.7554/eLife.89066 
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Anastos, M., Fabian, D., Müyesser, A., &; Szabó, T. (2023). Splitting matchings and the Ryser-Brualdi-Stein conjecture for multisets. Electronic Journal of Combinatorics. Electronic Journal of Combinatorics.https://doi.org/10.37236/11714 
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arXiv: 2212.03100

Sun, Y., Fu, H., Chatterjee, K., &; Goharshady, A. K. (2023). Automated tail bound analysis for probabilistic recurrence relations. In Computer Aided Verification (Vol. 13966, pp. 16–39). Paris, France: Springer Nature.https://doi.org/10.1007/978-3-031-37709-9_2 
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Akshay, S., Chatterjee, K., Meggendorfer, T., &; Zikelic, D. (2023). MDPs as distribution transformers: Affine invariant synthesis for safety objectives. In International Conference on Computer Aided Verification (Vol. 13966, pp. 86–112). Paris, France: Springer Nature.https://doi.org/10.1007/978-3-031-37709-9_5 
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Nagano, M., Aoshima, K., Shimamura, H., Siekhaus, D. E., Toshima, J. Y., &; Toshima, J. (2023). Distinct role of TGN-resident clathrin adaptors for Vpp activation in the TGN-endosome trafficking pathway. Journal of Cell Science. The Company of Biologists.https://doi.org/10.1242/jcs.261448 
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Henderson, P. M., Ghazaryan, A., Zibrov, A. A., Young, A. F., &; Serbyn, M. (2023). Deep learning extraction of band structure parameters from density of states: A case study on trilayer graphene. Physical Review B. American Physical Society.https://doi.org/10.1103/physrevb.108.125411 
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arXiv: 2210.06310

Brighi, P. (2023). Ergodicity breaking in disordered and kinetically constrained quantum many-body systems. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:12732 
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Brighi, P., Ljubotina, M., &; Serbyn, M. (2023). Hilbert space fragmentation and slow dynamics in particle-conserving quantum East models. SciPost Physics. SciPost Foundation.https://doi.org/10.21468/scipostphys.15.3.093 
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arXiv: 2210.15607