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
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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 
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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 
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arXiv: 2211.03515

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.
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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.
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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.
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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.
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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 
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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.
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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 
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arXiv: 2203.04579

Rojas Vega, M. N., De Castro, P., &; Soto, R. (2023). Mixtures of self-propelled particles interacting with asymmetric obstacles. The European Physical Journal E. Springer Nature.https://doi.org/10.1140/epje/s10189-023-00354-y 
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PMID: 37819444

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 
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arXiv: 2202.05088

Henzinger, T. A., Kueffner, K., &; Mallik, K. (2023). Monitoring algorithmic fairness under partial observations. In 23rd International Conference on Runtime Verification (Vol. 14245, pp. 291–311). Thessaloniki, Greece: Springer Nature.https://doi.org/10.1007/978-3-031-44267-4_15 
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arXiv: 2308.00341

Jakubík, J., Phuong, M., Chvosteková, M., &; Krakovská, A. (2023). Against the flow of time with multi-output models. Measurement Science Review. Sciendo.https://doi.org/10.2478/msr-2023-0023 
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Schumann, G., Andreassen, O. A., Banaschewski, T., Calhoun, V. D., Clinton, N., Desrivieres, S.,https://doi.org/10.1001/jamapsychiatry.2023.2996__Westlye, L. (2023). Addressing global environmental challenges to mental health using population neuroscience: A review. JAMA Psychiatry. American Medical Association.https://doi.org/10.1001/jamapsychiatry.2023.2996 
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Brooks, M., &; Seiringer, R. (2023). The Fröhlich Polaron at strong coupling: Part I - The quantum correction to the classical energy. Communications in Mathematical Physics. Springer Nature.https://doi.org/10.1007/s00220-023-04841-3 
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arXiv: 2207.03156

Kolmogorov, V. (2023). Solving relaxations of MAP-MRF problems: Combinatorial in-face Frank-Wolfe directions. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Vol. 2023, pp. 11980–11989). Vancouver, Canada: IEEE.https://doi.org/10.1109/CVPR52729.2023.01153 
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arXiv: 2010.09567

Lyons, D. B., Briffa, A., He, S., Choi, J., Hollwey, E., Colicchio, J.,https://doi.org/10.1016/j.celrep.2023.112132__Zilberman, D. (2023). Extensive de novo activity stabilizes epigenetic inheritance of CG methylation in Arabidopsis transposons. Cell Reports. Elsevier.https://doi.org/10.1016/j.celrep.2023.112132 
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Mitrouskas, D. J., Mysliwy, K., &; Seiringer, R. (2023). Optimal parabolic upper bound for the energy-momentum relation of a strongly coupled polaron. Forum of Mathematics. Cambridge University Press.https://doi.org/10.1017/fms.2023.45 
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arXiv: 2203.02454

Kicheva, A., &; Briscoe, J. (2023). Control of tissue development by morphogens. Annual Review of Cell and Developmental Biology. Annual Reviews.https://doi.org/10.1146/annurev-cellbio-020823-011522 
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PMID: 37418774