Summary
I am a Lecturer in Statistics in the Department of Mathematics at Imperial College London.
My research is in the field of statistical machine learning. I am particularly interested in sequential decision making problems, where the goal is to learn to make optimal decisions by sequentially interacting with an unknown environment. Some examples of problems I have worked on include variants of the multi-armed bandit, online learning, and reinforcement learning problems.
For further details of my research please see my personal website.
Publications
Journals
Lugosi G, Pike-Burke C, Savalle P-A, 2023, Bandit problems with fidelity rewards, Journal of Machine Learning Research, Vol:24, ISSN:1532-4435, Pages:1-44
Conference
Johnson E, Pike-Burke C, Rebeschini P, Sample-efficiency in multi-batch reinforcement learning: the need for dimension-dependent adaptivity, International Conference on Learning Representations (ICLR 2024), ICLR
Vakili S, Ahmed D, Bernacchia A, et al., 2023, Delayed feedback in kernel bandits, 40th International Conference on Machine Learning, ML Research Press, Pages:34779-34792, ISSN:2640-3498
van der Hoeven D, Pike-Burke C, Qiu H, et al., 2023, Trading-off payments and accuracy in online classification with paid stochastic experts, 40th International Conference on Machine Learning, ML Research Press, Pages:34809-34830, ISSN:2640-3498
Howson B, Pike-Burke C, Filippi S, 2023, Delayed feedback in generalised linear bandits revisited, Artificial Intelligence and Statistics s (AISTATS 2023), PMLR, Pages:1-25, ISSN:2640-3498