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
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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

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.
LibreCat: https://research-explorer.ista.ac.at/record/14181
DOI: 10.24963/ijcai.2021/322
arXiv: 2105.09240

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.
LibreCat: https://research-explorer.ista.ac.at/record/14179
arXiv: 2106.04619

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.
LibreCat: https://research-explorer.ista.ac.at/record/14180
arXiv: 2110.06399

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.
LibreCat: https://research-explorer.ista.ac.at/record/14117
DOI: 10.1109/jproc.2021.3058954
arXiv: 2102.11107

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.
LibreCat: https://research-explorer.ista.ac.at/record/14178
arXiv: 2010.14407

Enforcing and discovering structure in machine learning
F. Locatello, ArXiv (n.d.).
LibreCat: https://research-explorer.ista.ac.at/record/14221
DOI: 10.48550/arXiv.2111.13693
arXiv: 2111.13693

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.
LibreCat: https://research-explorer.ista.ac.at/record/14332

Local strong Birkhoff conjecture and local spectral rigidity of almost every ellipse
I. Koval, ArXiv (n.d.).
LibreCat: https://research-explorer.ista.ac.at/record/14278
DOI: 10.48550/ARXIV.2111.12171
arXiv: 2111.12171

Improved verification techniques for concurrent systems
V. Toman, Improved Verification Techniques for Concurrent Systems, Institute of Science and Technology Austria, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10199
DOI: 10.15479/at:ista:10199

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).
LibreCat: https://research-explorer.ista.ac.at/record/8429
DOI: 10.1038/s41467-021-27258-9
WoS: 000724450600023

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.
LibreCat: https://research-explorer.ista.ac.at/record/10854
DOI: 10.1145/3410220.3453923
arXiv: 2005.07637

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.
LibreCat: https://research-explorer.ista.ac.at/record/10855
DOI: 10.1145/3447384
arXiv: 2005.07637

Algorithms and conditional lower bounds for planning problems
K. Chatterjee, W. Dvoák, M.H. Henzinger, A. Svozil, Artificial Intelligence 297 (2021).
LibreCat: https://research-explorer.ista.ac.at/record/9293
DOI: 10.1016/j.artint.2021.103499
WoS: 000657537500003
arXiv: 1804.07031