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
Boyer, N., Baudry, D. and Rebeschini, P. (2025) “Best-of-Both Worlds for linear contextual bandits with paid observations”, arXiv.
Baudry, D. et al. (2025) “Does stochastic gradient really succeed for bandits?”, in.
Alfano, C. et al. (2025) “Meta-Learning Objectives for Preference Optimization”, in.
Farghly, T. et al. (2025) “Implicit Regularisation in Diffusion Models: An Algorithm-Dependent Generalisation Analysis”, arXiv.
Buna-Marginean, A. and Rebeschini, P. (2025) “Robust gradient descent for phase retrieval”, in Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS) 2025. PMLR.