Barton, N. H. (2023). The infinitesimal model with dominance. Institute of Science and Technology Austria.https://doi.org/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.

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.

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.

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.

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 

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.

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

Rao, P., Mallikarjun, B. R., Fox, G., Weyrich, T., Bickel, B., Pfister, H.,https://doi.org/10.1007/s11263-023-01899-3__Elgharib, M. (2023). A deeper analysis of volumetric relightiable faces. International Journal of Computer Vision. Springer Nature.https://doi.org/10.1007/s11263-023-01899-3 

Buri, P., Fatichi, S., Shaw, T., Miles, E. S., McCarthy, M., Fyffe, C. L.,https://doi.org/10.1029/2022WR033841__Pellicciotti, F. (2023). Land surface modeling in the Himalayas: On the importance of evaporative fluxes for the water balance of a high-elevation catchment. Water Resources Research. Wiley.https://doi.org/10.1029/2022WR033841