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.
Monaci M, Pike-Burke C, Santini A, 2022, Exact algorithms for the 0-1 Time-Bomb Knapsack Problem, Computers and Operations Research, Vol:145, ISSN:0305-0548
et al., 2021, Local differential privacy for regret minimization in reinforcement learning, Advances in Neural Information Processing Systems, NeurIPS, Pages:1-13, ISSN:1049-5258
Neu G, Pike-Burke C, 2020, A unifying view of optimism in episodic reinforcement learning, Neural Information Processing Systems (NeurIPS 2020), ISSN:1049-5258
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