Dale Schuurmans
Appointment
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
About
Appointed Canada CIFAR AI Chair – 2021
Dale Schuurmans is a Canada CIFAR AI Chair at Amii, a Research Director at Google DeepMind and a professor in the Department of Computing Science at the University of Alberta.
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.
Awards
- ICLR Outstanding Paper Award, 2024
- 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
Relevant Publications
- Du, Y., Yang, M., Dai, B., Dai, H., Nachum, O., Tenenbaum, J., Schuurmans, D. and Abbeel, P. (2023). Learning universal policies via text-guided video generation. In Advances in Neural Information Processing Systems 36.
- Zhou, D., Schaerli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Bousquet, O., Le, Q. and Chi, E. (2023). Least-to-most prompting enables complex reasoning in large language models. In Proceedings of the International Conference on Learning Representations.
- Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A. and Zhou, D. (2023). Self-consistency improves chain of thought reasoning in language models. In Proceedings of the International Conference on Learning Representations.
- Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. and Zhou, D. (2022). Chain of thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing
- Ren, H., Dai, H., Dai, B., Chen, X., Yasunaga, M., Sun, H., Schuurmans, D., Leskovec, J., and Zhou, D. (2021). LEGO: Latent execution-guided reasoning for multi-hop question answering on knowledge graphs. In Proceedings of the International Conference on Machine Learning.
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.