Yaoliang Yu is a Canada CIFAR AI Chair at the Vector Institute and an assistant professor in the Cheriton School of Computer Science at the University of Waterloo.
Yu’s main research interests include robust regression and classification, representation learning, kernel methods, generative models, convex and nonconvex optimization, distributed systems, and applications in computer vision, genetics, and finance.
- PhD Dissertation Award from the Canadian Artificial Intelligence Association, 2015
- Top reviewers for ICML/NeurIPS, 2018
Chang, X., Yu, Y. L., Yang, Y., & Xing, E. P. (2016). Semantic pooling for complex event analysis in untrimmed videos. IEEE transactions on pattern analysis and machine intelligence, 39(8), 1617-1632.
Chang, X., Yang, Y., Hauptmann, A., Xing, E. P., & Yu, Y. L. (2015). Semantic concept discovery for large-scale zero-shot event detection. In Twenty-fourth international joint conference on artificial intelligence.
Xing, E. P., Ho, Q., Dai, W., Kim, J. K., Wei, J., Lee, S., … & Yu, Y. (2015). Petuum: A new platform for distributed machine learning on big data. IEEE transactions on Big Data, 1(2), 49-67.
White, M., Yu, Y., Zhang, X., & Schuurmans, D. (2012). Convex Multi-view Subspace Learning. In Nips (pp. 1682-1690).
Zhang, X., Yu, Y., & Schuurmans, D. (2012). Accelerated Training for Matrix-norm Regularization: A Boosting Approach. In NIPS (Vol. 12, pp. 2915-2923).
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.