| 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 | 10182* | 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 | 2349* | 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 | 1098 | 2022 |
| An optimistic perspective on offline reinforcement learning R Agarwal, D Schuurmans, M Norouzi International conference on machine learning, 104-114, 2020 | 714* | 2020 |
| Maximum margin clustering L Xu, J Neufeld, B Larson, D Schuurmans Advances in neural information processing systems 17, 2004 | 639 | 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 | 543 | 2017 |
| Advances in Large-Margin Classifiers PJ Bartlett, B Schölkopf, D Schuurmans, AJ Smola MIT Press 155, 156, 2000 | 491* | 2000 |
| Learning with a Strong Adversary R Huang, B Xu, D Schuurmans, C Szepesvari https://arxiv.org/abs/1511.03034, 2015 | 465 | 2015 |
| Automatic Gait Optimization With Gaussian Process Regression. DJ Lizotte, T Wang, MH Bowling, D Schuurmans IJCAI 7, 944-949, 2007 | 416 | 2007 |
| 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 | 403 | 2022 |
| Augmenting naive bayes classifiers with statistical language models F Peng, D Schuurmans, S Wang Information Retrieval 7, 317-345, 2004 | 393 | 2004 |
| Boosting in the limit: Maximizing the margin of learned ensembles AJ Grove, D Schuurmans AAAI/IAAI, 692-699, 1998 | 386 | 1998 |
| Discriminative batch mode active learning Y Guo, D Schuurmans Advances in neural information processing systems 20, 2007 | 383 | 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 | 314 | 2020 |
| 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 | 284* | 2003 |
| Combining Naive Bayes and n-Gram Language Models for Text Classification F Peng, D Schuurmans European Conference on Information Retrieval, 335-350, 2003 | 282 | 2003 |
| 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 | 272 | 2019 |
| Reward augmented maximum likelihood for neural structured prediction M Norouzi, S Bengio, N Jaitly, M Schuster, Y Wu, D Schuurmans Advances In Neural Information Processing Systems 29, 2016 | 261 | 2016 |
| Algaedice: Policy gradient from arbitrary experience O Nachum, B Dai, I Kostrikov, Y Chow, L Li, D Schuurmans arXiv preprint arXiv:1912.02074, 2019 | 259 | 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 | 251 | 2013 |
Bo DaiGoogle Brain & Georgia Tech在 google.com 的电子邮件经过验证
