Limits on the Adaptive Security of Yao’s Garbling
C. Kamath Hosdurg, K. Klein, K.Z. Pietrzak, D. Wichs, in:, 41st Annual International Cryptology Conference, Part II , Springer Nature, Cham, 2021, pp. 486–515.
LibreCat: https://research-explorer.ista.ac.at/record/10041
DOI: 10.1007/978-3-030-84245-1_17

Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement
K. Klein, G. Pascual Perez, M. Walter, C. Kamath Hosdurg, M. Capretto, M. Cueto Noval, I. Markov, M.X. Yeo, J.F. Alwen, K.Z. Pietrzak, in:, 2021 IEEE Symposium on Security and Privacy , IEEE, 2021, pp. 268–284.
LibreCat: https://research-explorer.ista.ac.at/record/10049
DOI: 10.1109/sp40001.2021.00035

On treewidth, separators and Yao's garbling
C. Kamath Hosdurg, K. Klein, K.Z. Pietrzak, in:, 19th Theory of Cryptography Conference 2021, International Association for Cryptologic Research, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10044

Towards designer materials using customizable particle shape
A. Piankov, Towards Designer Materials Using Customizable Particle Shape, Institute of Science and Technology Austria, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10422
DOI: 10.15479/at:ista:10422

Fairness through regularization for learning to rank
N.H. Konstantinov, C. Lampert, ArXiv (n.d.).
LibreCat: https://research-explorer.ista.ac.at/record/10803
DOI: 10.48550/arXiv.2102.05996
arXiv: 2102.05996

Artificial neural network states for non-additive systems
W. Rzadkowski, M. Lemeshko, J.H. Mentink, ArXiv (n.d.).
LibreCat: https://research-explorer.ista.ac.at/record/10762
DOI: 10.48550/arXiv.2105.15193
arXiv: 2105.15193

Underspecification in deep learning
M. Phuong, Underspecification in Deep Learning, Institute of Science and Technology Austria, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/9418
DOI: 10.15479/AT:ISTA:9418

On disentangled representations learned from correlated data
F. Träuble, E. Creager, N. Kilbertus, F. Locatello, A. Dittadi, A. Goyal, B. Schölkopf, S. Bauer, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 10401–10412.
LibreCat: https://research-explorer.ista.ac.at/record/14177
arXiv: 2006.07886

Neighborhood contrastive learning applied to online patient monitoring
H. Yèche, G. Dresdner, F. Locatello, M. Hüser, G. Rätsch, in:, Proceedings of 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 11964–11974.
LibreCat: https://research-explorer.ista.ac.at/record/14176
arXiv: 2106.05142

Backward-compatible prediction updates: A probabilistic approach
F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, P. Gehler, in:, 35th Conference on Neural Information Processing Systems, 2021, pp. 116–128.
LibreCat: https://research-explorer.ista.ac.at/record/14182
arXiv: 2107.01057