Barbier, J., Camilli, F., Mondelli, M., &; Sáenz, M. (2023). Fundamental limits in structured principal component analysis and how to reach them. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences.https://doi.org/10.1073/pnas.2302028120 
LibreCat: https://research-explorer.ista.ac.at/record/13315
DOI: 10.1073/pnas.2302028120
PMID: 37463204

Vardi, O., Maroudas-Sklare, N., Kolodny, Y., Volosniev, A., Saragovi, A., Galili, N.,https://doi.org/10.1073/pnas.2300828120__Paltiel, Y. (2023). Nuclear spin effects in biological processes. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences.https://doi.org/10.1073/pnas.2300828120 
LibreCat: https://research-explorer.ista.ac.at/record/14037
DOI: 10.1073/pnas.2300828120
PMID: 37523549

Dubach, G., &; Erdös, L. (2023). Dynamics of a rank-one perturbation of a Hermitian matrix. Electronic Communications in Probability. Institute of Mathematical Statistics.https://doi.org/10.1214/23-ECP516 
LibreCat: https://research-explorer.ista.ac.at/record/12683
DOI: 10.1214/23-ECP516
WoS: 000950650200005
arXiv: 2108.13694

Cipolloni, G., Erdös, L., &; Schröder, D. J. (2023). Functional central limit theorems for Wigner matrices. Annals of Applied Probability. Institute of Mathematical Statistics.https://doi.org/10.1214/22-AAP1820 
LibreCat: https://research-explorer.ista.ac.at/record/12761
DOI: 10.1214/22-AAP1820
WoS: 000946432400015
arXiv: 2012.13218

Browning, T. D., Boudec, P. L., &; Sawin, W. (2023). The Hasse principle for random Fano hypersurfaces. Annals of Mathematics. Princeton University.https://doi.org/10.4007/annals.2023.197.3.3 
LibreCat: https://research-explorer.ista.ac.at/record/8682
DOI: 10.4007/annals.2023.197.3.3
WoS: 000966611000003
arXiv: 2006.02356

Mckerral, J. C., Kleshnina, M., Ejov, V., Bartle, L., Mitchell, J. G., &; Filar, J. A. (2023). Empirical parameterisation and dynamical analysis of the allometric Rosenzweig-MacArthur equations. PLoS One. Public Library of Science.https://doi.org/10.1371/journal.pone.0279838 
LibreCat: https://research-explorer.ista.ac.at/record/12706
DOI: 10.1371/journal.pone.0279838
WoS: 000996122900022
PMID: 36848357

Eguchi, K., Le Monnier, E., &; Shigemoto, R. (2023). Nanoscale phosphoinositide distribution on cell membranes of mouse cerebellar neurons. The Journal of Neuroscience. Society for Neuroscience.https://doi.org/10.1523/JNEUROSCI.1514-22.2023 
LibreCat: https://research-explorer.ista.ac.at/record/13202
DOI: 10.1523/JNEUROSCI.1514-22.2023
WoS: 001020132100005
PMID: 37160366

Bonolis, D., &; Browning, T. D. (2023). Uniform bounds for rational points on hyperelliptic fibrations. Annali Della Scuola Normale Superiore Di Pisa - Classe Di Scienze. Scuola Normale Superiore - Edizioni della Normale.https://doi.org/10.2422/2036-2145.202010_018 
LibreCat: https://research-explorer.ista.ac.at/record/12916
DOI: 10.2422/2036-2145.202010_018
arXiv: 2007.14182

Roos, B. (2023). Boundary superconductivity in BCS theory. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:14374 
LibreCat: https://research-explorer.ista.ac.at/record/14374
DOI: 10.15479/at:ista:14374

Hainzl, C., Roos, B., &; Seiringer, R. (2023). Boundary superconductivity in the BCS model. Journal of Spectral Theory. EMS Press.https://doi.org/10.4171/JST/439 
LibreCat: https://research-explorer.ista.ac.at/record/13207
DOI: 10.4171/JST/439
WoS: 000997933500008
arXiv: 2201.08090

Barton, N. H., Etheridge, A. M., &; Véber, A. (2023). The infinitesimal model with dominance. Genetics. Oxford Academic.https://doi.org/10.1093/genetics/iyad133 
LibreCat: https://research-explorer.ista.ac.at/record/14452
DOI: 10.1093/genetics/iyad133
arXiv: 2211.03515

Barton, N. H. (2023). The infinitesimal model with dominance. Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:12949 
LibreCat: https://research-explorer.ista.ac.at/record/12949
DOI: 10.15479/AT:ISTA:12949

Markov, I., Vladu, A., Guo, Q., &; Alistarh, D.-A. (2023). Quantized distributed training of large models with convergence guarantees. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 24020–24044). Honolulu, Hawaii, HI, United States: ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/14461
arXiv: 2302.02390

Fichtenberger, H., Henzinger, M. H., &; Upadhyay, J. (2023). Constant matters: Fine-grained error bound on differentially private continual observation. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 10072–10092). Honolulu, Hawaii, HI, United States: ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/14462

Shevchenko, A., Kögler, K., Hassani, H., &; Mondelli, M. (2023). Fundamental limits of two-layer autoencoders, and achieving them with gradient methods. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 31151–31209). Honolulu, Hawaii, HI, United States: ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/14459
arXiv: 2212.13468

Nikdan, M., Pegolotti, T., Iofinova, E. B., Kurtic, E., &; Alistarh, D.-A. (2023). SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 26215–26227). Honolulu, Hawaii, HI, United States: ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/14460
arXiv: 2302.04852

Hoffmann, C., &; Simkin, M. (2023). Stronger lower bounds for leakage-resilient secret sharing. In 8th International Conference on Cryptology and Information Security in Latin America (Vol. 14168, pp. 215–228). Quito, Ecuador: Springer Nature.https://doi.org/10.1007/978-3-031-44469-2_11 
LibreCat: https://research-explorer.ista.ac.at/record/14457
DOI: 10.1007/978-3-031-44469-2_11

Frantar, E., &; Alistarh, D.-A. (2023). SparseGPT: Massive language models can be accurately pruned in one-shot. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 10323–10337). Honolulu, Hawaii, HI, United States: ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/14458
arXiv: 2301.00774

Cornalba, F., Disselkamp, C., Scassola, D., &; Helf, C. (2023). Multi-objective reward generalization: improving performance of Deep Reinforcement Learning for applications in single-asset trading. Neural Computing and Applications. Springer Nature.https://doi.org/10.1007/s00521-023-09033-7 
LibreCat: https://research-explorer.ista.ac.at/record/14451
DOI: 10.1007/s00521-023-09033-7
arXiv: 2203.04579

Kwan, M. A., Sah, A., Sawhney, M., &; Simkin, M. (2023). Substructures in Latin squares. Israel Journal of Mathematics. Springer Nature.https://doi.org/10.1007/s11856-023-2513-9 
LibreCat: https://research-explorer.ista.ac.at/record/14444
DOI: 10.1007/s11856-023-2513-9
arXiv: 2202.05088