| 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 | 1492* | 2022 |
| Maximum margin clustering L Xu, J Neufeld, B Larson, D Schuurmans Advances in neural information processing systems 17, 2004 | 613 | 2004 |
| Advances in Large-Margin Classifiers PJ Bartlett, B Schölkopf, D Schuurmans, AJ Smola MIT Press 155, 156, 2000 | 484* | 2000 |
| An optimistic perspective on offline reinforcement learning R Agarwal, D Schuurmans, M Norouzi International Conference on Machine Learning, 104-114, 2020 | 477* | 2020 |
| 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 | 432 | 2017 |
| Learning with a Strong Adversary R Huang, B Xu, D Schuurmans, C Szepesvari https://arxiv.org/abs/1511.03034, 2015 | 409 | 2015 |
| Augmenting naive bayes classifiers with statistical language models F Peng, D Schuurmans, S Wang Information Retrieval 7, 317-345, 2004 | 375 | 2004 |
| Boosting in the limit: Maximizing the margin of learned ensembles AJ Grove, D Schuurmans AAAI/IAAI, 692-699, 1998 | 373 | 1998 |
| Discriminative batch mode active learning Y Guo, D Schuurmans Advances in neural information processing systems 20, 2007 | 370 | 2007 |
| Automatic Gait Optimization With Gaussian Process Regression. DJ Lizotte, T Wang, MH Bowling, D Schuurmans IJCAI 7, 944-949, 2007 | 368 | 2007 |
| 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 | 351* | 2022 |
| 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 | 269* | 2003 |
| Combining Naive Bayes and n-Gram Language Models for Text Classification F Peng, D Schuurmans European Conference on Information Retrieval, 335-350, 2003 | 261 | 2003 |
| 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 | 246 | 2022 |
| 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 | 225 | 2016 |
| Unsupervised and semi-supervised multi-class support vector machines L Xu, D Schuurmans AAAI 40, 50, 2005 | 218 | 2005 |
| Robust support vector machine training via convex outlier ablation L Xu, K Crammer, D Schuurmans AAAI 6, 536-542, 2006 | 208 | 2006 |
| 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 | 207 | 2020 |
| Constraint-based optimization and utility elicitation using the minimax decision criterion C Boutilier, R Patrascu, P Poupart, D Schuurmans Artificial Intelligence 170 (8-9), 686-713, 2006 | 203 | 2006 |
| 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 | 188 | 2019 |
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