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Piankov, Anton. “Towards Designer Materials Using Customizable Particle Shape.”https://doi.org/10.15479/at:ista:10422__Institute of Science and Technology Austria, 2021.https://doi.org/10.15479/at:ista:10422 .
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Konstantinov, Nikola H, and Christoph Lampert. “Fairness through Regularization for Learning to Rank.”https://doi.org/10.48550/arXiv.2102.05996__ArXiv, n.d.https://doi.org/10.48550/arXiv.2102.05996 .
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Rzadkowski, Wojciech, Mikhail Lemeshko, and Johan H. Mentink. “Artificial Neural Network States for Non-Additive Systems.”https://doi.org/10.48550/arXiv.2105.15193__ArXiv, n.d.https://doi.org/10.48550/arXiv.2105.15193 .
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Phuong, Mary. “Underspecification in Deep Learning.”https://doi.org/10.15479/AT:ISTA:9418__Institute of Science and Technology Austria, 2021.https://doi.org/10.15479/AT:ISTA:9418 .
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Träuble, Frederik, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, and Stefan Bauer. “On Disentangled Representations Learned from Correlated Data.” In Proceedings of the 38th International Conference on Machine Learning, 139:10401–12. ML Research Press, 2021.
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Yèche, Hugo, Gideon Dresdner, Francesco Locatello, Matthias Hüser, and Gunnar Rätsch. “Neighborhood Contrastive Learning Applied to Online Patient Monitoring.” In Proceedings of 38th International Conference on Machine Learning, 139:11964–74. ML Research Press, 2021.
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Träuble, Frederik, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, and Peter Gehler. “Backward-Compatible Prediction Updates: A Probabilistic Approach.” In 35th Conference on Neural Information Processing Systems, 34:116–28, 2021.
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Dresdner, Gideon, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, and Gunnar Rätsch. “Boosting Variational Inference with Locally Adaptive Step-Sizes.”https://doi.org/10.24963/ijcai.2021/322__In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2337–43. International Joint Conferences on Artificial Intelligence, 2021.https://doi.org/10.24963/ijcai.2021/322 .
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Kügelgen, Julius von, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, and Francesco Locatello. “Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.” In Advances in Neural Information Processing Systems, 34:16451–67, 2021.
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Rahaman, Nasim, Muhammad Waleed Gondal, Shruti Joshi, Peter Gehler, Yoshua Bengio, Francesco Locatello, and Bernhard Schölkopf. “Dynamic Inference with Neural Interpreters.” In Advances in Neural Information Processing Systems, 34:10985–98, 2021.
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Scholkopf, Bernhard, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. “Toward Causal Representation Learning.”https://doi.org/10.1109/jproc.2021.3058954__Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 2021.https://doi.org/10.1109/jproc.2021.3058954 .
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Dittadi, Andrea, Frederik Träuble, Francesco Locatello, Manuel Wüthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, and Bernhard Schölkopf. “On the Transfer of Disentangled Representations in Realistic Settings.” In The Ninth International Conference on Learning Representations, 2021.
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Locatello, Francesco. “Enforcing and Discovering Structure in Machine Learning.”https://doi.org/10.48550/arXiv.2111.13693__ArXiv, n.d.https://doi.org/10.48550/arXiv.2111.13693 .
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arXiv: 2111.13693

Träuble, Frederik, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer. “Representation Learning for Out-of-Distribution Generalization in Reinforcement Learning.” In ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021.
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Koval, Illya. “Local Strong Birkhoff Conjecture and Local Spectral Rigidity of Almost Every Ellipse.”https://doi.org/10.48550/ARXIV.2111.12171__ArXiv, n.d.https://doi.org/10.48550/ARXIV.2111.12171 .
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Toman, Viktor. “Improved Verification Techniques for Concurrent Systems.”https://doi.org/10.15479/at:ista:10199__Institute of Science and Technology Austria, 2021.https://doi.org/10.15479/at:ista:10199 .
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Patxot, Marion, Daniel Trejo Banos, Athanasios Kousathanas, Etienne J Orliac, Sven E Ojavee, Gerhard Moser, Julia Sidorenko, et al. “Probabilistic Inference of the Genetic Architecture Underlying Functional Enrichment of Complex Traits.”https://doi.org/10.1038/s41467-021-27258-9__Nature Communications. Springer Nature, 2021.https://doi.org/10.1038/s41467-021-27258-9 .
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Foerster, Klaus-Tycho, Janne Korhonen, Ami Paz, Joel Rybicki, and Stefan Schmid. “Input-Dynamic Distributed Algorithms for Communication Networks.”https://doi.org/10.1145/3410220.3453923__In Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, 71–72. Association for Computing Machinery, 2021.https://doi.org/10.1145/3410220.3453923 .
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Foerster, Klaus-Tycho, Janne Korhonen, Ami Paz, Joel Rybicki, and Stefan Schmid. “Input-Dynamic Distributed Algorithms for Communication Networks.”https://doi.org/10.1145/3447384__Proceedings of the ACM on Measurement and Analysis of Computing Systems. Association for Computing Machinery, 2021.https://doi.org/10.1145/3447384 .
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arXiv: 2005.07637