Geometric superinductance qubits: Controlling phase delocalization across a single Josephson junction
M. Peruzzo, F. Hassani, G. Szep, A. Trioni, E. Redchenko, M. Zemlicka, J.M. Fink, PRX Quantum 2 (2021) 040341.
LibreCat: https://research-explorer.ista.ac.at/record/9928
DOI: 10.1103/PRXQuantum.2.040341
WoS: 000723015100001
arXiv: 2106.05882

Discrete-to-continuum limits of transport problems and gradient flows in the space of measures
L. Portinale, Discrete-to-Continuum Limits of Transport Problems and Gradient Flows in the Space of Measures, Institute of Science and Technology Austria, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10030
DOI: 10.15479/at:ista:10030

Geometric superinductors and their applications in circuit quantum electrodynamics
M. Peruzzo, Geometric Superinductors and Their Applications in Circuit Quantum Electrodynamics, Institute of Science and Technology Austria, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/9920
DOI: 10.15479/at:ista:9920

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.
LibreCat: https://research-explorer.ista.ac.at/record/10432
arXiv: 2001.05918

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

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