Peruzzo, Matilda. Geometric Superinductors and Their Applications in Circuit Quantum Electrodynamics. Institute of Science and Technology Austria, 2021, doi:10.15479/at:ista:9920.
LibreCat: https://research-explorer.ista.ac.at/record/9920
DOI: 10.15479/at:ista:9920

Nadiradze, Giorgi, et al. “Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent.” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 10, 2021, pp. 9037–45.
LibreCat: https://research-explorer.ista.ac.at/record/10432
arXiv: 2001.05918

Kamath Hosdurg, Chethan, et al. “Limits on the Adaptive Security of Yao’s Garbling.”10.1007/978-3-030-84245-1_17__41st Annual International Cryptology Conference, Part II , vol. 12826, Springer Nature, 2021, pp. 486–515, doi:10.1007/978-3-030-84245-1_17.
LibreCat: https://research-explorer.ista.ac.at/record/10041
DOI: 10.1007/978-3-030-84245-1_17

Klein, Karen, et al. “Keep the Dirt: Tainted TreeKEM, Adaptively and Actively Secure Continuous Group Key Agreement.”10.1109/sp40001.2021.00035__2021 IEEE Symposium on Security and Privacy , IEEE, 2021, pp. 268–84, doi:10.1109/sp40001.2021.00035.
LibreCat: https://research-explorer.ista.ac.at/record/10049
DOI: 10.1109/sp40001.2021.00035

Kamath Hosdurg, Chethan, et al. “On Treewidth, Separators and Yao’s Garbling.” 19th Theory of Cryptography Conference 2021, 2021/926, International Association for Cryptologic Research, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10044

Piankov, Anton. Towards Designer Materials Using Customizable Particle Shape. Institute of Science and Technology Austria, 2021, doi:10.15479/at:ista:10422.
LibreCat: https://research-explorer.ista.ac.at/record/10422
DOI: 10.15479/at:ista:10422

Konstantinov, Nikola H., and Christoph Lampert. “Fairness through Regularization for Learning to Rank.”10.48550/arXiv.2102.05996__ArXiv, 2102.05996, doi:10.48550/arXiv.2102.05996.
LibreCat: https://research-explorer.ista.ac.at/record/10803
DOI: 10.48550/arXiv.2102.05996
arXiv: 2102.05996

Rzadkowski, Wojciech, et al. “Artificial Neural Network States for Non-Additive Systems.”10.48550/arXiv.2105.15193__ArXiv, doi:10.48550/arXiv.2105.15193.
LibreCat: https://research-explorer.ista.ac.at/record/10762
DOI: 10.48550/arXiv.2105.15193
arXiv: 2105.15193

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

Träuble, Frederik, et al. “On Disentangled Representations Learned from Correlated Data.” Proceedings of the 38th International Conference on Machine Learning, vol. 139, ML Research Press, 2021, pp. 10401–12.
LibreCat: https://research-explorer.ista.ac.at/record/14177
arXiv: 2006.07886

Yèche, Hugo, et al. “Neighborhood Contrastive Learning Applied to Online Patient Monitoring.” Proceedings of 38th International Conference on Machine Learning, vol. 139, ML Research Press, 2021, pp. 11964–74.
LibreCat: https://research-explorer.ista.ac.at/record/14176
arXiv: 2106.05142

Träuble, Frederik, et al. “Backward-Compatible Prediction Updates: A Probabilistic Approach.” 35th Conference on Neural Information Processing Systems, vol. 34, 2021, pp. 116–28.
LibreCat: https://research-explorer.ista.ac.at/record/14182
arXiv: 2107.01057

Dresdner, Gideon, et al. “Boosting Variational Inference with Locally Adaptive Step-Sizes.”10.24963/ijcai.2021/322__Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, 2021, pp. 2337–43, doi:10.24963/ijcai.2021/322.
LibreCat: https://research-explorer.ista.ac.at/record/14181
DOI: 10.24963/ijcai.2021/322
arXiv: 2105.09240

Kügelgen, Julius von, et al. “Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.” Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 16451–67.
LibreCat: https://research-explorer.ista.ac.at/record/14179
arXiv: 2106.04619

Rahaman, Nasim, et al. “Dynamic Inference with Neural Interpreters.” Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 10985–98.
LibreCat: https://research-explorer.ista.ac.at/record/14180
arXiv: 2110.06399

Scholkopf, Bernhard, et al. “Toward Causal Representation Learning.”10.1109/jproc.2021.3058954__Proceedings of the IEEE, vol. 109, no. 5, Institute of Electrical and Electronics Engineers, 2021, pp. 612–34, doi:10.1109/jproc.2021.3058954.
LibreCat: https://research-explorer.ista.ac.at/record/14117
DOI: 10.1109/jproc.2021.3058954
arXiv: 2102.11107

Dittadi, Andrea, et al. “On the Transfer of Disentangled Representations in Realistic Settings.” The Ninth International Conference on Learning Representations, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/14178
arXiv: 2010.14407

Locatello, Francesco. “Enforcing and Discovering Structure in Machine Learning.”10.48550/arXiv.2111.13693__ArXiv, 2111.13693, doi:10.48550/arXiv.2111.13693.
LibreCat: https://research-explorer.ista.ac.at/record/14221
DOI: 10.48550/arXiv.2111.13693
arXiv: 2111.13693

Träuble, Frederik, et al. “Representation Learning for Out-of-Distribution Generalization in Reinforcement Learning.” ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/14332

Koval, Illya. “Local Strong Birkhoff Conjecture and Local Spectral Rigidity of Almost Every Ellipse.”10.48550/ARXIV.2111.12171__ArXiv, 2111.12171, doi:10.48550/ARXIV.2111.12171.
LibreCat: https://research-explorer.ista.ac.at/record/14278
DOI: 10.48550/ARXIV.2111.12171
arXiv: 2111.12171