Chris Maddison is a Canada CIFAR AI Chair at the Vector Institute and an assistant professor at the department of computer science and statistical sciences at the University of Toronto.
Maddison works on methods for machine learning with an emphasis on those that work at scale in deep learning applications. He is particularly interested in methods for numerical integration and optimization. So far Maddison has worked on gradient estimation, variational inference, Monte Carlo methods, and first-order methods for optimization.
- Open Philanthropy AI Fellow, 2018
- IJCAI Marvin Minsky Medal for Outstanding Achievements in AI (AlphaGo Team, 2018)
- Cannes Lion International Festival of Creativity, Grand Prix (AlphaGo Team, 2016)
- Google DeepMind Scholarship, 2016
- Best Paper Award, Neural Information and Processing Systems (NIPS), 2014
Mathieu, E., Lan, C. L., Maddison, C. J., Tomioka, R., & Teh, Y. W. (2019). Continuous Hierarchical Representations with Poincar’e Variational Auto-Encoders.
Choi, D., Shallue, C. J., Nado, Z., Lee, J., Maddison, C. J., & Dahl, G. E. (2019). On empirical comparisons of optimizers for deep learning.
Garnelo, M., Rosenbaum, D., Maddison, C., Ramalho, T., Saxton, D., Shanahan, M., … & Eslami, S. A. (2018). Conditional neural processes. In International Conference on Machine Learning (pp. 1704-1713). PMLR.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., … & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. nature, 529(7587), 484-489.
Maddison, C. J., Mnih, A., & Teh, Y. W. (2016). The concrete distribution: A continuous relaxation of discrete random variables.
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.