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
Johnson, E. et al. (2025) “Stochastic Shortest Path with Sparse Adversarial Costs.”
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.
Liu, X. et al. (2025) “Non-stationary Bandit Convex Optimization: A Comprehensive Study.”
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.