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.
et al., Local differential privacy for regret minimization in reinforcement learning, Advances in Neural Information Processing Systems, ISSN:1049-5258
Neu G, Pike-Burke C, A unifying view of optimism in episodic reinforcement learning, Neural Information Processing Systems (NeurIPS 2020)
Pike-Burke C, Grunewalder S, 2019, Recovering bandits, 33rd Conference on Neural Information Processing Systems (NeurIPS), Neural Information Processing Systems (NIPS), Pages:1-10, ISSN:1049-5258
et al., 2018, Bandits with delayed, aggregated anonymous feedback, 35th International Conference on Machine Learning, PMLR, Pages:4105-4113, ISSN:2640-3498
Pike-Burke C, Grunewalder S, 2017, Optimistic planning for the stochastic knapsack problem, 20th International Conference on Artificial Intelligence and Statistics (AISTATS), MICROTOME PUBLISHING, Pages:1114-1122, ISSN:2640-3498