Elastic consistency: A practical consistency model for distributed stochastic gradient descent
G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, D.-A. Alistarh, in:, Proceedings of the AAAI Conference on Artificial Intelligence, 2021, pp. 9037–9045.

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.

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.

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.

Towards designer materials using customizable particle shape
A. Piankov, Towards Designer Materials Using Customizable Particle Shape, Institute of Science and Technology Austria, 2021.

Fairness through regularization for learning to rank
N.H. Konstantinov, C. Lampert, ArXiv (n.d.).

Artificial neural network states for non-additive systems
W. Rzadkowski, M. Lemeshko, J.H. Mentink, ArXiv (n.d.).

Underspecification in deep learning
M. Phuong, Underspecification in Deep Learning, Institute of Science and Technology Austria, 2021.

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.

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.

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.

Boosting variational inference with locally adaptive step-sizes
G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, G. Rätsch, in:, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, 2021, pp. 2337–2343.

Self-supervised learning with data augmentations provably isolates content from style
J. von Kügelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve, F. Locatello, in:, Advances in Neural Information Processing Systems, 2021, pp. 16451–16467.

Dynamic inference with neural interpreters
N. Rahaman, M.W. Gondal, S. Joshi, P. Gehler, Y. Bengio, F. Locatello, B. Schölkopf, in:, Advances in Neural Information Processing Systems, 2021, pp. 10985–10998.

Toward causal representation learning
B. Scholkopf, F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, Y. Bengio, Proceedings of the IEEE 109 (2021) 612–634.

On the transfer of disentangled representations in realistic settings
A. Dittadi, F. Träuble, F. Locatello, M. Wüthrich, V. Agrawal, O. Winther, S. Bauer, B. Schölkopf, in:, The Ninth International Conference on Learning Representations, 2021.

Enforcing and discovering structure in machine learning
F. Locatello, ArXiv (n.d.).

Representation learning for out-of-distribution generalization in reinforcement learning
F. Träuble, A. Dittadi, M. Wuthrich, F. Widmaier, P.V. Gehler, O. Winther, F. Locatello, O. Bachem, B. Schölkopf, S. Bauer, in:, ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021.

Local strong Birkhoff conjecture and local spectral rigidity of almost every ellipse
I. Koval, ArXiv (n.d.).

Improved verification techniques for concurrent systems
V. Toman, Improved Verification Techniques for Concurrent Systems, Institute of Science and Technology Austria, 2021.