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5750 Publications
2021 | Conference Paper | IST-REx-ID: 9987 | 
P. Agarwal, K. Chatterjee, S. Pathak, A. Pavlogiannis, and V. Toman, “Stateless model checking under a reads-value-from equivalence,” in 33rd International Conference on Computer-Aided Verification , Virtual, 2021, vol. 12759, pp. 341–366.
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2021 | Thesis | IST-REx-ID: 10007 |
S. Hensel, “Curvature driven interface evolution: Uniqueness properties of weak solution concepts,” Institute of Science and Technology Austria, 2021.
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2021 | Journal Article | IST-REx-ID: 10191 |
T. L. Bui, K. Chatterjee, T. Gautam, A. Pavlogiannis, and V. Toman, “The reads-from equivalence for the TSO and PSO memory models,” Proceedings of the ACM on Programming Languages, vol. 5, no. OOPSLA. Association for Computing Machinery, 2021.
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2021 | Preprint | IST-REx-ID: 10013 |
S. Hensel and T. Laux, “Weak-strong uniqueness for the mean curvature flow of double bubbles,” arXiv. .
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2021 | Journal Article | IST-REx-ID: 9928 |
M. Peruzzo et al., “Geometric superinductance qubits: Controlling phase delocalization across a single Josephson junction,” PRX Quantum, vol. 2, no. 4. American Physical Society, p. 040341, 2021.
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2021 | Thesis | IST-REx-ID: 10030 |
L. Portinale, “Discrete-to-continuum limits of transport problems and gradient flows in the space of measures,” Institute of Science and Technology Austria, 2021.
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2021 | Thesis | IST-REx-ID: 9920 |
M. Peruzzo, “Geometric superinductors and their applications in circuit quantum electrodynamics,” Institute of Science and Technology Austria, 2021.
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2021 | Conference Paper | IST-REx-ID: 10432 |
G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, and D.-A. Alistarh, “Elastic consistency: A practical consistency model for distributed stochastic gradient descent,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 10, pp. 9037–9045.
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2021 | Conference Paper | IST-REx-ID: 10041 |
C. Kamath Hosdurg, K. Klein, K. Z. Pietrzak, and D. Wichs, “Limits on the Adaptive Security of Yao’s Garbling,” in 41st Annual International Cryptology Conference, Part II , Virtual, 2021, vol. 12826, pp. 486–515.
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2021 | Conference Paper | IST-REx-ID: 10049 |
K. Klein et al., “Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement,” in 2021 IEEE Symposium on Security and Privacy , San Francisco, CA, United States, 2021, pp. 268–284.
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2021 | Conference Paper | IST-REx-ID: 10044 |
C. Kamath Hosdurg, K. Klein, and K. Z. Pietrzak, “On treewidth, separators and Yao’s garbling,” in 19th Theory of Cryptography Conference 2021, Raleigh, NC, United States, 2021.
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2021 | Thesis | IST-REx-ID: 10422 |
A. Piankov, “Towards designer materials using customizable particle shape,” Institute of Science and Technology Austria, 2021.
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2021 | Preprint | IST-REx-ID: 10803 |
N. H. Konstantinov and C. Lampert, “Fairness through regularization for learning to rank,” arXiv. .
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2021 | Preprint | IST-REx-ID: 10762 |
W. Rzadkowski, M. Lemeshko, and J. H. Mentink, “Artificial neural network states for non-additive systems,” arXiv. .
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2021 | Thesis | IST-REx-ID: 9418 |
M. Phuong, “Underspecification in deep learning,” Institute of Science and Technology Austria, 2021.
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2021 | Conference Paper | IST-REx-ID: 14177 |
F. Träuble et al., “On disentangled representations learned from correlated data,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 10401–10412.
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2021 | Conference Paper | IST-REx-ID: 14176 |
H. Yèche, G. Dresdner, F. Locatello, M. Hüser, and G. Rätsch, “Neighborhood contrastive learning applied to online patient monitoring,” in Proceedings of 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 11964–11974.
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2021 | Conference Paper | IST-REx-ID: 14182 |
F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, and P. Gehler, “Backward-compatible prediction updates: A probabilistic approach,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 116–128.
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2021 | Conference Paper | IST-REx-ID: 14181 |
G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, and G. Rätsch, “Boosting variational inference with locally adaptive step-sizes,” in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Montreal, Canada, 2021, pp. 2337–2343.
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2021 | Conference Paper | IST-REx-ID: 14179 |
J. von Kügelgen et al., “Self-supervised learning with data augmentations provably isolates content from style,” in Advances in Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 16451–16467.
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