| A tutorial on thompson sampling D Russo, B Van Roy, A Kazerouni, I Osband, Z Wen Foundations and Trends in Machine Learning 11 (1), 1–96, 2018 | 1189 | 2018 |
| Learning to optimize via posterior sampling D Russo, B Van Roy Mathematics of Operations Research 39 (4), 1221-1243, 2014 | 764 | 2014 |
| An information-theoretic analysis of thompson sampling D Russo, B Van Roy Journal of Machine Learning Research 17 (68), 1-30, 2016 | 433 | 2016 |
| A finite time analysis of temporal difference learning with linear function approximation J Bhandari, D Russo, R Singal Operations Research 69 (3), 950--973, 2021 | 415 | 2021 |
| How much does your data exploration overfit? Controlling bias via information usage. D Russo, J Zou IEEE Transactions on Information Theory, 2019 | 372* | 2019 |
| Learning to optimize via information-directed sampling D Russo, B Van Roy Operations Research 66 (1), 230-252, 2018 | 348* | 2018 |
| Deep exploration via randomized value functions I Osband, B Van Roy, DJ Russo, Z Wen Journal of Machine Learning Research 20 (124), 1-62, 2019 | 342 | 2019 |
| Simple Bayesian Algorithms for Best-Arm Identification D Russo Operations Research 68 (6), 1625--1647, 2020 | 321* | 2020 |
| Eluder Dimension and the Sample Complexity of Optimistic Exploration. D Russo, B Van Roy Advances in Neural Information Processing Systems 26, 2256-2264, 2013 | 273 | 2013 |
| Global optimality guarantees for policy gradient methods J Bhandari, D Russo Operations Research, 2024 | 265 | 2024 |
| Improving the expected improvement algorithm C Qin, D Klabjan, D Russo Advances in Neural Information Processing Systems, 5382--5392, 2017 | 160 | 2017 |
| (More) efficient reinforcement learning via posterior sampling I Osband, D Russo, B Van Roy Advances in Neural Information Processing Systems 26, 2013 | 106 | 2013 |
| On the linear convergence of policy gradient methods for finite mdps J Bhandari, D Russo International Conference on Artificial Intelligence and Statistics, 2386-2394, 2021 | 102* | 2021 |
| Worst-case regret bounds for exploration via randomized value functions D Russo Advances in Neural Information Processing Systems 32, 2019 | 96 | 2019 |
| Satisficing in time-sensitive bandit learning D Russo, B Van Roy Mathematics of Operations Research 47 (4), 2815-2839, 2022 | 66* | 2022 |
| Adaptive Experimentation in the Presence of Exogenous Nonstationary Variation C Qin, D Russo arXiv preprint arXiv:2202.09036, 2022 | 39* | 2022 |
| A note on the equivalence of upper confidence bounds and gittins indices for patient agents D Russo Operations Research 69 (1), 273-278, 2021 | 17 | 2021 |
| Impatient bandits: Optimizing recommendations for the long-term without delay TM McDonald, L Maystre, M Lalmas, D Russo, K Ciosek Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 13 | 2023 |
| Policy gradient optimization of Thompson sampling policies S Min, CC Moallemi, DJ Russo arXiv preprint arXiv:2006.16507, 2020 | 13 | 2020 |
| Approximation benefits of policy gradient methods with aggregated states D Russo Management Science 69 (11), 6898-6911, 2023 | 12 | 2023 |
