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Dale Schuurmans
Dale Schuurmans
University of Alberta, Google
Verified email at cs.ualberta.ca - Homepage
Title
Cited by
Cited by
Year
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
6132004
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
4322017
Learning with a Strong Adversary
R Huang, B Xu, D Schuurmans, C Szepesvari
https://arxiv.org/abs/1511.03034, 2015
4092015
Augmenting naive bayes classifiers with statistical language models
F Peng, D Schuurmans, S Wang
Information Retrieval 7, 317-345, 2004
3752004
Boosting in the limit: Maximizing the margin of learned ensembles
AJ Grove, D Schuurmans
AAAI/IAAI, 692-699, 1998
3731998
Discriminative batch mode active learning
Y Guo, D Schuurmans
Advances in neural information processing systems 20, 2007
3702007
Automatic Gait Optimization With Gaussian Process Regression.
DJ Lizotte, T Wang, MH Bowling, D Schuurmans
IJCAI 7, 944-949, 2007
3682007
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
2612003
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
2462022
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
2252016
Unsupervised and semi-supervised multi-class support vector machines
L Xu, D Schuurmans
AAAI 40, 50, 2005
2182005
Robust support vector machine training via convex outlier ablation
L Xu, K Crammer, D Schuurmans
AAAI 6, 536-542, 2006
2082006
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
2072020
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
2032006
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
1882019
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