| Bandits with delayed, aggregated anonymous feedback C Pike-Burke, S Agrawal, C Szepesvari, S Grunewalder International Conference on Machine Learning, 4105-4113, 2018 | 115 | 2018 |
| Multi-objective optimization C Pike-Burke Report accessible through www. researchgate. net, 2019 | 71 | 2019 |
| A unifying view of optimism in episodic reinforcement learning G Neu, C Pike-Burke Advances in Neural Information Processing Systems 33, 1392-1403, 2020 | 64 | 2020 |
| Recovering bandits C Pike-Burke, S Grunewalder Advances in Neural Information Processing Systems 32, 2019 | 45 | 2019 |
| Local differential privacy for regret minimization in reinforcement learning E Garcelon, V Perchet, C Pike-Burke, M Pirotta Advances in Neural Information Processing Systems 34, 10561-10573, 2021 | 32 | 2021 |
| Delayed feedback in episodic reinforcement learning B Howson, C Pike-Burke, S Filippi arXiv preprint arXiv:2111.07615, 2021 | 10 | 2021 |
| Delayed feedback in generalised linear bandits revisited B Howson, C Pike-Burke, S Filippi International Conference on Artificial Intelligence and Statistics, 6095-6119, 2023 | 8 | 2023 |
| Optimal convergence rate for exact policy mirror descent in discounted markov decision processes E Johnson, C Pike-Burke, P Rebeschini Advances in Neural Information Processing Systems 36, 2024 | 6 | 2024 |
| Optimistic planning for the stochastic knapsack problem C Pike-Burke, S Grunewalder Artificial Intelligence and Statistics, 1114-1122, 2017 | 6 | 2017 |
| Bandits with delayed anonymous feedback C Pike-Burke, S Agrawal, C Szepesvari, S Grünewälder stat 1050, 20, 2017 | 5 | 2017 |
| Optimism and delays in episodic reinforcement learning B Howson, C Pike-Burke, S Filippi International Conference on Artificial Intelligence and Statistics, 6061-6094, 2023 | 3 | 2023 |
| Exact algorithms for the 0–1 Time-bomb Knapsack Problem M Monaci, C Pike-Burke, A Santini Computers & Operations Research 145, 105848, 2022 | 3 | 2022 |
| Delayed feedback in kernel bandits S Vakili, D Ahmed, A Bernacchia, C Pike-Burke International Conference on Machine Learning, 34779-34792, 2023 | 2 | 2023 |
| Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity E Johnson, C Pike-Burke, P Rebeschini arXiv preprint arXiv:2310.01616, 2023 | 1 | 2023 |
| Active Learning for Quantum Mechanical Measurements R Zhu, C Pike-Burke, F Mintert arXiv preprint arXiv:2212.07513, 2022 | 1 | 2022 |
| Reinforcement learning with digital human models of varying visual characteristics N Bhatia, CM Pike-Burke, EM Normando, OK Matar Proceedings of the 7th International Digital Human Modeling Symposium 7 (1), 2022 | 1 | 2022 |
| Sample Complexity of Goal-Conditioned Hierarchical Reinforcement Learning A Robert, C Pike-Burke, AA Faisal Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| Delayed Feedback in Generalised Linear Bandits B Howson, C Pike-Burke, SL Filippi Sixteenth European Workshop on Reinforcement Learning, 2023 | | 2023 |
| Sample Complexity of Hierarchical Decompositions in Markov Decision Processes A Robert, C Pike-Burke, AA Faisal ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, 2023 | | 2023 |
| Trading-off payments and accuracy in online classification with paid stochastic experts D Van Der Hoeven, C Pike-Burke, H Qiu, N Cesa-Bianchi International Conference on Machine Learning, 34809-34830, 2023 | | 2023 |
