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Dale Schuurmans
Dale Schuurmans
University of Alberta, Google DeepMind
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
9669*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
2266*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
10662022
An optimistic perspective on offline reinforcement learning
R Agarwal, D Schuurmans, M Norouzi
International conference on machine learning, 104-114, 2020
701*2020
Maximum margin clustering
L Xu, J Neufeld, B Larson, D Schuurmans
Advances in neural information processing systems 17, 2004
6392004
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
5372017
Advances in Large-Margin Classifiers
PJ Bartlett, B Schölkopf, D Schuurmans, AJ Smola
MIT Press 155, 156, 2000
489*2000
Learning with a Strong Adversary
R Huang, B Xu, D Schuurmans, C Szepesvari
https://arxiv.org/abs/1511.03034, 2015
4642015
Automatic Gait Optimization With Gaussian Process Regression.
DJ Lizotte, T Wang, MH Bowling, D Schuurmans
IJCAI 7, 944-949, 2007
4132007
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
3972022
Augmenting naive bayes classifiers with statistical language models
F Peng, D Schuurmans, S Wang
Information Retrieval 7, 317-345, 2004
3932004
Boosting in the limit: Maximizing the margin of learned ensembles
AJ Grove, D Schuurmans
AAAI/IAAI, 692-699, 1998
3851998
Discriminative batch mode active learning
Y Guo, D Schuurmans
Advances in neural information processing systems 20, 2007
3832007
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
3122020
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
2822003
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
2642019
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
2592016
Algaedice: Policy gradient from arbitrary experience
O Nachum, B Dai, I Kostrikov, Y Chow, L Li, D Schuurmans
arXiv preprint arXiv:1912.02074, 2019
2572019
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
2452013
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