Professor Patrick Rebeschini
Professor of Statistics and Machine Learning
Biographical Sketch
I have a Ph.D. in Operations Research and Financial Engineering from Princeton University (2014). After that, I joined the Yale Institute for Network Science at Yale University. I worked two years as a Postdoctoral Associate in the Electrical Engineering Department, and one year as an Associate Research Scientist with a joint appointment as a Lecturer in the Computer Science Department at Yale.
Research Interests
My research interests lie at the intersection of probability, statistics, and computer science. I am interested in the investigation of fundamental principles in high-dimensional probability, statistics and optimisation to design computationally efficient and statistically optimal algorithms for machine learning.
Publications
Vary, S., Martínez-Rubio, D. and Rebeschini, P. (2024) “Black-Box Uniform Stability for Non-Euclidean Empirical Risk Minimization”, arXiv.
Alfano, C. et al. (2024) “Learning Loss Landscapes in Preference Optimization”, arXiv.
Huh, J. and Rebeschini, P. (2024) “Generalization bounds for label noise stochastic gradient descent”, in Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024). PMLR, pp. 1360–1368.
Johnson, E., Pike-Burke, C. and Rebeschini, P. (2024) “Sample-efficiency in multi-batch reinforcement learning: the need for dimension-dependent adaptivity”, in Proceedings of the International Conference on Learning Representations (ICLR 2024). OpenReview.
Johnson, E., Pike-Burke, C. and Rebeschini, P. (2023) “Optimal convergence rate for exact policy mirror descent in discounted Markov decision processes”, in Advances in Neural Information Processing Systems. NeurIPS, pp. 76496–76524.