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5750 Publications
2021 | Thesis | IST-REx-ID: 10030 | 
Portinale, L. (2021). Discrete-to-continuum limits of transport problems and gradient flows in the space of measures. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10030
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2021 | Thesis | IST-REx-ID: 9920 |
Peruzzo, M. (2021). Geometric superinductors and their applications in circuit quantum electrodynamics. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:9920
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2021 | Conference Paper | IST-REx-ID: 10432 |
Nadiradze, G., Markov, I., Chatterjee, B., Kungurtsev, V., & Alistarh, D.-A. (2021). Elastic consistency: A practical consistency model for distributed stochastic gradient descent. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, pp. 9037–9045). Virtual.
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2021 | Conference Paper | IST-REx-ID: 10041 |
Kamath Hosdurg, C., Klein, K., Pietrzak, K. Z., & Wichs, D. (2021). Limits on the Adaptive Security of Yao’s Garbling. In 41st Annual International Cryptology Conference, Part II (Vol. 12826, pp. 486–515). Cham: Springer Nature. https://doi.org/10.1007/978-3-030-84245-1_17
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2021 | Conference Paper | IST-REx-ID: 10049 |
Klein, K., Pascual Perez, G., Walter, M., Kamath Hosdurg, C., Capretto, M., Cueto Noval, M., … Pietrzak, K. Z. (2021). Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement. In 2021 IEEE Symposium on Security and Privacy (pp. 268–284). San Francisco, CA, United States: IEEE. https://doi.org/10.1109/sp40001.2021.00035
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2021 | Conference Paper | IST-REx-ID: 10044 |
Kamath Hosdurg, C., Klein, K., & Pietrzak, K. Z. (2021). On treewidth, separators and Yao’s garbling. In 19th Theory of Cryptography Conference 2021. Raleigh, NC, United States: International Association for Cryptologic Research.
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2021 | Thesis | IST-REx-ID: 10422 |
Piankov, A. (2021). Towards designer materials using customizable particle shape. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10422
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2021 | Preprint | IST-REx-ID: 10803 |
Konstantinov, N. H., & Lampert, C. (n.d.). Fairness through regularization for learning to rank. arXiv. https://doi.org/10.48550/arXiv.2102.05996
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2021 | Preprint | IST-REx-ID: 10762 |
Rzadkowski, W., Lemeshko, M., & Mentink, J. H. (n.d.). Artificial neural network states for non-additive systems. arXiv. https://doi.org/10.48550/arXiv.2105.15193
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2021 | Thesis | IST-REx-ID: 9418 |
Phuong, M. (2021). Underspecification in deep learning. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:9418
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2021 | Conference Paper | IST-REx-ID: 14177 |
Träuble, F., Creager, E., Kilbertus, N., Locatello, F., Dittadi, A., Goyal, A., … Bauer, S. (2021). On disentangled representations learned from correlated data. In Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 10401–10412). Virtual: ML Research Press.
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2021 | Conference Paper | IST-REx-ID: 14176 |
Yèche, H., Dresdner, G., Locatello, F., Hüser, M., & Rätsch, G. (2021). Neighborhood contrastive learning applied to online patient monitoring. In Proceedings of 38th International Conference on Machine Learning (Vol. 139, pp. 11964–11974). Virtual: ML Research Press.
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2021 | Conference Paper | IST-REx-ID: 14182 |
Träuble, F., Kügelgen, J. von, Kleindessner, M., Locatello, F., Schölkopf, B., & Gehler, P. (2021). Backward-compatible prediction updates: A probabilistic approach. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 116–128). Virtual.
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2021 | Conference Paper | IST-REx-ID: 14181 |
Dresdner, G., Shekhar, S., Pedregosa, F., Locatello, F., & Rätsch, G. (2021). Boosting variational inference with locally adaptive step-sizes. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (pp. 2337–2343). Montreal, Canada: International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2021/322
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2021 | Conference Paper | IST-REx-ID: 14179 |
Kügelgen, J. von, Sharma, Y., Gresele, L., Brendel, W., Schölkopf, B., Besserve, M., & Locatello, F. (2021). Self-supervised learning with data augmentations provably isolates content from style. In Advances in Neural Information Processing Systems (Vol. 34, pp. 16451–16467). Virtual.
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2021 | Conference Paper | IST-REx-ID: 14180 |
Rahaman, N., Gondal, M. W., Joshi, S., Gehler, P., Bengio, Y., Locatello, F., & Schölkopf, B. (2021). Dynamic inference with neural interpreters. In Advances in Neural Information Processing Systems (Vol. 34, pp. 10985–10998). Virtual.
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2021 | Journal Article | IST-REx-ID: 14117 |
Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021). Toward causal representation learning. Proceedings of the IEEE. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/jproc.2021.3058954
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2021 | Conference Paper | IST-REx-ID: 14178 |
Dittadi, A., Träuble, F., Locatello, F., Wüthrich, M., Agrawal, V., Winther, O., … Schölkopf, B. (2021). On the transfer of disentangled representations in realistic settings. In The Ninth International Conference on Learning Representations. Virtual.
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2021 | Preprint | IST-REx-ID: 14221 |
Locatello, F. (n.d.). Enforcing and discovering structure in machine learning. arXiv. https://doi.org/10.48550/arXiv.2111.13693
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2021 | Preprint | IST-REx-ID: 14278 |
Koval, I. (n.d.). Local strong Birkhoff conjecture and local spectral rigidity of almost every ellipse. arXiv. https://doi.org/10.48550/ARXIV.2111.12171
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