My research is on sequential decision making problems under uncertainty and potentially limited feedback. In particular, I work on multi-armed bandit, reinforcement learning, and online learning problems. Often the problems I work on are motivated by issues arising in applications such as education and healthcare
Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke and Matteo Pirotta, Local Differentially Private Regret Minimization in Reinforcement Learning .
G.Neu and C.Pike-Burke, A Unifying View of Optimism in Episodic Reinforcement Learning, in Neural Information Processing Systems (NeurIPS),2020 (to appear). *
C.Pike-Burke and S.Grünewälder, Recovering Bandits, in Neural Information Processing Systems (NeurIPS), 2019.
C.Pike-Burke, S.Agrawal, C.Szepesvári and S.Grünewälder, Bandits with Delayed, Aggregated Anonymous Feedback, in International Conference on Machine Learning (ICML), 2018.
C.Pike-Burke and S.Grünewälder, Optimistic Planning for the Stochastic Knapsack Problem, in International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.
C.Pike-Burke and S.Grünewälder, Recovering Bandits, in European Workshop on Reinforcement Learning (EWRL), 2018.
C.Pike-Burke and S.Grünewälder, Optimistic Planning for Question Selection, in NeurIPS workshop on Machine Learning for Education, 2016.
My PhD focused on sequential decision problems arising from the problem of selecting questions to give to students in education software. In particular, I studied several variants of the multi-armed bandit problem specifically motivated by issues arising in education software. My thesis was in collaboration with Sparx and can be accessed here.
* indicates alphabetical ordering of authors