Dale Schuurmans is a Canada CIFAR AI Chair at Amii, a professor in the Department of Computing Science at the University of Alberta, and a Senior Staff Research Scientist at Google Brain in Edmonton, Canada.
Schuurmans’ long-term research goal is to develop systems that learn predictive models from massive data sources when the requisite models are complex. Some of his ongoing research include statistical natural language modelling, reinforcement learning, and learning search control. Schuurmans is currently focusing on developing algorithms that acquire competence through the integration of demonstration-based and experienced-based learning. He has also developed new methods for probabilistic inference, optimization, and constraint satisfaction.
- NeurIPS Best Paper Award, 2018
- Fellow, Association for the Advancement of Artificial Intelligence (AAAI), 2017
- Canada Research Chair in Machine Learning, 2008-2018
- IJCAI Distinguished Paper Award, 2005
- AAAI Outstanding Paper Award, 2000
Mei, J., Xiao, C., Dai, B., Li, L., Szepesvári, C., & Schuurmans, D. (2020). Escaping the Gravitational Pull of Softmax. Advances in Neural Information Processing Systems, 33.
Yang, M., Nachum, O., Dai, B., Li, L., & Schuurmans, D. (2020). Off-policy evaluation via the regularized lagrangian. arXiv preprint arXiv:2007.03438.
Wen, J., Dai, B., Li, L., & Schuurmans, D. (2020). Batch Stationary Distribution Estimation. arXiv preprint arXiv:2003.00722.
Chen, M., Gummadi, R., Harris, C., & Schuurmans, D. (2019). Surrogate objectives for batch policy optimization in one-step decision making. In Advances in Neural Information Processing Systems (pp. 8827-8837).
Lu, T., Schuurmans, D., & Boutilier, C. (2018). Non-delusional Q-learning and value-iteration. Advances in neural information processing systems, 31, 9949-9959.
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.