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.).

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.

Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits
M. Patxot, D. Trejo Banos, A. Kousathanas, E.J. Orliac, S.E. Ojavee, G. Moser, J. Sidorenko, Z. Kutalik, R. Magi, P.M. Visscher, L. Ronnegard, M.R. Robinson, Nature Communications 12 (2021).

Input-dynamic distributed algorithms for communication networks
K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, S. Schmid, in:, Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, Association for Computing Machinery, 2021, pp. 71–72.

Input-dynamic distributed algorithms for communication networks
K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, S. Schmid, Proceedings of the ACM on Measurement and Analysis of Computing Systems 5 (2021) 1–33.

Algorithms and conditional lower bounds for planning problems
K. Chatterjee, W. Dvoák, M.H. Henzinger, A. Svozil, Artificial Intelligence 297 (2021).