| Chain of thought prompting elicits reasoning in large language models J Wei, X Wang, D Schuurmans, M Bosma, E Chi, Q Le, D Zhou Advances in Neural Information Processing Systems 35, 2022 | 26104 | 2022 |
| Self-consistency improves chain of thought reasoning in language models X Wang, J Wei, D Schuurmans, Q Le, E Chi, S Narang, A Chowdhery, ... arXiv preprint arXiv:2203.11171, 2022 | 6174* | 2022 |
| Least-to-most prompting enables complex reasoning in large language models D Zhou, N Schärli, L Hou, J Wei, N Scales, X Wang, D Schuurmans, C Cui, ... arXiv preprint arXiv:2205.10625, 2022 | 2167 | 2022 |
| An optimistic perspective on offline reinforcement learning R Agarwal, D Schuurmans, M Norouzi International conference on machine learning, 104-114, 2020 | 855 | 2020 |
| What learning algorithm is in-context learning? investigations with linear models E Akyürek, D Schuurmans, J Andreas, T Ma, D Zhou arXiv preprint arXiv:2211.15661, 2022 | 714 | 2022 |
| Maximum margin clustering L Xu, J Neufeld, B Larson, D Schuurmans Advances in neural information processing systems 17, 2004 | 652 | 2004 |
| Bridging the gap between value and policy based reinforcement learning O Nachum, M Norouzi, K Xu, D Schuurmans Advances in neural information processing systems 30, 2017 | 643 | 2017 |
| Learning with a Strong Adversary R Huang, B Xu, D Schuurmans, C Szepesvari https://arxiv.org/abs/1511.03034, 2015 | 493 | 2015 |
| Advances in Large-Margin Classifiers PJ Bartlett, B Schölkopf, D Schuurmans, AJ Smola MIT Press 155, 156, 2000 | 472* | 2000 |
| Automatic Gait Optimization With Gaussian Process Regression. DJ Lizotte, T Wang, MH Bowling, D Schuurmans IJCAI 7, 944-949, 2007 | 454 | 2007 |
| On the global convergence rates of softmax policy gradient methods J Mei, C Xiao, C Szepesvari, D Schuurmans International conference on machine learning, 6820-6829, 2020 | 412 | 2020 |
| Sft memorizes, rl generalizes: A comparative study of foundation model post-training T Chu, Y Zhai, J Yang, S Tong, S Xie, D Schuurmans, QV Le, S Levine, ... arXiv preprint arXiv:2501.17161, 2025 | 410 | 2025 |
| Augmenting naive bayes classifiers with statistical language models F Peng, D Schuurmans, S Wang Information Retrieval 7 (3), 317-345, 2004 | 402 | 2004 |
| Learning universal policies via text-guided video generation Y Du, S Yang, B Dai, H Dai, O Nachum, J Tenenbaum, D Schuurmans, ... Advances in neural information processing systems 36, 9156-9172, 2023 | 391 | 2023 |
| Discriminative batch mode active learning Y Guo, D Schuurmans Advances in neural information processing systems 20, 2007 | 387 | 2007 |
| Boosting in the limit: Maximizing the margin of learned ensembles AJ Grove, D Schuurmans AAAI/IAAI, 692-699, 1998 | 382 | 1998 |
| Understanding the impact of entropy on policy optimization Z Ahmed, N Le Roux, M Norouzi, D Schuurmans International conference on machine learning, 151-160, 2019 | 366 | 2019 |
| Systolic peak detection in acceleration photoplethysmograms measured from emergency responders in tropical conditions M Elgendi, I Norton, M Brearley, D Abbott, D Schuurmans PloS one 8 (10), e76585, 2013 | 314 | 2013 |
| Algaedice: Policy gradient from arbitrary experience O Nachum, B Dai, I Kostrikov, Y Chow, L Li, D Schuurmans arXiv preprint arXiv:1912.02074, 2019 | 299 | 2019 |
| Language independent authorship attribution with character level n-grams F Peng, D Schuurmans, V Keselj, S Wang 10th Conference of the European Chapter of the Association for Computational …, 2003 | 295* | 2003 |
Bo DaiGoogle Brain & Georgia TechVerified email at google.com
