| Bandits with delayed, aggregated anonymous feedback C Pike-Burke, S Agrawal, C Szepesvari, S Grunewalder International Conference on Machine Learning, 4105-4113, 2018 | 139 | 2018 |
| A unifying view of optimism in episodic reinforcement learning G Neu, C Pike-Burke Advances in Neural Information Processing Systems 33, 1392-1403, 2020 | 84 | 2020 |
| Multi-objective optimization C Pike-Burke Report accessible through www. researchgate. net, 2019 | 74 | 2019 |
| Recovering bandits C Pike-Burke, S Grunewalder Advances in Neural Information Processing Systems 32, 2019 | 51 | 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 | 50 | 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 | 19 | 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, 76496-76524, 2023 | 17 | 2023 |
| Delayed feedback in episodic reinforcement learning B Howson, C Pike-Burke, S Filippi arXiv preprint arXiv:2111.07615, 2021 | 12 | 2021 |
| Delayed feedback in kernel bandits S Vakili, D Ahmed, A Bernacchia, C Pike-Burke International Conference on Machine Learning, 34779-34792, 2023 | 7 | 2023 |
| Exact algorithms for the 0–1 time-bomb knapsack problem M Monaci, C Pike-Burke, A Santini Computers & Operations Research 145, 105848, 2022 | 7 | 2022 |
| Optimism and delays in episodic reinforcement learning B Howson, C Pike-Burke, S Filippi International Conference on Artificial Intelligence and Statistics, 6061-6094, 2023 | 6 | 2023 |
| Bandits with delayed anonymous feedback C Pike-Burke, S Agrawal, C Szepesvari, S Grünewälder stat 1050, 20, 2017 | 6 | 2017 |
| Optimistic planning for the stochastic knapsack problem C Pike-Burke, S Grunewalder Artificial Intelligence and Statistics, 1114-1122, 2017 | 6 | 2017 |
| Sample complexity of goal-conditioned hierarchical reinforcement learning A Robert, C Pike-Burke, AA Faisal Advances in Neural Information Processing Systems 36, 62696-62712, 2023 | 5 | 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 | 2 | 2023 |
| Active learning for quantum mechanical measurements R Zhu, C Pike-Burke, F Mintert Physical Review A 109 (6), 062404, 2024 | 1 | 2024 |
| 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 |
| Bandit problems with fidelity rewards G Lugosi, C Pike-Burke, PA Savalle arXiv preprint arXiv:2111.13026, 2021 | 1 | 2021 |
| When and why randomised exploration works (in linear bandits) M Abeille, D Janz, C Pike-Burke arXiv preprint arXiv:2502.08870, 2025 | | 2025 |
| Fixed-Budget Change Point Identification in Piecewise Constant Bandits J Lazzaro, C Pike-Burke arXiv preprint arXiv:2501.12957, 2025 | | 2025 |
