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

Clerico, E., Flynn, H. and Rebeschini, P. (2026) “Uniform mean estimation for monotonic processes”, Statistics & Probability Letters, 228, pp. 110558–110558.

Johnson, E. et al. (2025) “Stochastic Shortest Path with Sparse Adversarial Costs.”

LÃ©vy, L. et al. (2025) “Self-Concordant Perturbations for Linear Bandits”, arXiv.

Boyer, N., Baudry, D. and Rebeschini, P. (2025) “Best-of-Both Worlds for linear contextual bandits with paid observations”, arXiv.

Alfano, C. et al. (2025) “Meta-Learning Objectives for Preference Optimization”, in.

Baudry, D. et al. (2025) “Does stochastic gradient really succeed for bandits?”, in.

Farghly, T. et al. (2025) “Implicit Regularisation in Diffusion Models: An Algorithm-Dependent Generalisation Analysis”, arXiv.

Johnson, E. et al. (2025) “On the necessity of adaptive regularisation:Optimal anytime online learning on $\boldsymbol{\ell_p}$-balls.”

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.

Contact Details

College affiliation: Tutorial Fellow at University College

Email: patrick.rebeschini@stats.ox.ac.uk

Web: https://www.stats.ox.ac.uk/~rebeschi/

Research Groups

Statistical Theory and Methodology

Computational Statistics and Machine Learning