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., 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.
Alfano, C., Yuan, R. and Rebeschini, P. (2023) “A novel framework for policy mirror descent with general parameterization and linear convergence”, in. Curran Associates.