| Superhuman AI for heads-up no-limit poker: Libratus beats top professionals N Brown, T Sandholm Science 359 (6374), 418-424, 2018 | 680 | 2018 |
| Superhuman AI for multiplayer poker N Brown, T Sandholm Science 365 (6456), 885-890, 2019 | 654 | 2019 |
| Safe and nested subgame solving for imperfect-information games N Brown, T Sandholm Neural Information Processing Systems, 2017 | 192* | 2017 |
| Deep counterfactual regret minimization N Brown, A Lerer, S Gross, T Sandholm International Conference on Machine Learning, 2019 | 188 | 2019 |
| Solving imperfect-information games via discounted regret minimization N Brown, T Sandholm Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 1829-1836, 2019 | 126 | 2019 |
| Libratus: The Superhuman AI for No-Limit Poker. N Brown, T Sandholm IJCAI, 5226-5228, 2017 | 105 | 2017 |
| Combining deep reinforcement learning and search for imperfect-information games N Brown, A Bakhtin, A Lerer, Q Gong Advances in Neural Information Processing Systems 33, 17057-17069, 2020 | 91 | 2020 |
| Hierarchical Abstraction, Distributed Equilibrium Computation, and Post-Processing, with Application to a Champion No-Limit Texas Hold'em Agent. N Brown, S Ganzfried, T Sandholm AAAI Workshop: Computer Poker and Imperfect Information 204, 2015 | 78 | 2015 |
| Depth-limited solving for imperfect-information games N Brown, T Sandholm, B Amos Advances in neural information processing systems 31, 2018 | 75 | 2018 |
| Improving Policies via Search in Cooperative Partially Observable Games A Lerer, H Hu, J Foerster, N Brown AAAI Conference on Artificial Intelligence, 2020 | 59 | 2020 |
| Dynamic thresholding and pruning for regret minimization N Brown, C Kroer, T Sandholm Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017 | 48 | 2017 |
| Off-belief learning H Hu, A Lerer, B Cui, L Pineda, N Brown, J Foerster International Conference on Machine Learning, 4369-4379, 2021 | 41 | 2021 |
| Reduced space and faster convergence in imperfect-information games via pruning N Brown, T Sandholm International conference on machine learning, 596-604, 2017 | 37* | 2017 |
| Simultaneous abstraction and equilibrium finding in games N Brown, TW Sandholm Carnegie Mellon University, 2015 | 36 | 2015 |
| Human-level play in the game of Diplomacy by combining language models with strategic reasoning Meta Fundamental AI Research Diplomacy Team (FAIR)†, A Bakhtin, ... Science 378 (6624), 1067-1074, 2022 | 35 | 2022 |
| DREAM: Deep regret minimization with advantage baselines and model-free learning E Steinberger, A Lerer, N Brown arXiv preprint arXiv:2006.10410, 2020 | 34 | 2020 |
| Regret-based pruning in extensive-form games N Brown, T Sandholm Advances in neural information processing systems 28, 2015 | 34 | 2015 |
| Regret transfer and parameter optimization N Brown, T Sandholm Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014 | 32 | 2014 |
| Stable-predictive optimistic counterfactual regret minimization G Farina, C Kroer, N Brown, T Sandholm International conference on machine learning, 1853-1862, 2019 | 30 | 2019 |
| Human-level performance in no-press diplomacy via equilibrium search J Gray, A Lerer, A Bakhtin, N Brown arXiv preprint arXiv:2010.02923, 2020 | 29 | 2020 |
