Yaoliang Yu is currently an associate professor in the David R. Cheriton School of Computer Science at University of Waterloo. He obtained his PhD from the computing science department of University of Alberta in 2013, and he spent two wonderful postdoctoral years at CMU. His main research interests include robust regression and classification, representation learning, kernel methods, generative models, convex and nonconvex optimization, distributed system, and applications in computer vision, natural languages, and finance.
Associate Professor, David R. Cheriton School of Computer Science, University of Waterloo
Canada CIFAR Artificial Intelligence Chair
Research Interests
- Robust methods
- Generative models
- Convex and nonconvex optimization
- Distributed optimization
Highlights
- PhD Dissertation Award from the Canadian Artificial Intelligence Association, 2015
- Top reviewers for ICML/NeurIPS, 2018
- ACM SIGSOFT Distinguished Paper Award 2020
Publications
The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing
2021
Optimality and stability in non-convex smooth games
2022
Are my deep learning systems fair? An empirical study of fixed-seed training
2021
S: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks
2021
Demystifying and Generalizing BinaryConnect
2021
Quantifying and Improving Transferability in Domain Generalization
2021
Conditional Generative Quantile Networks via Optimal Transport
2022
DEVIATE: A Deep Learning Variance Testing Framework
2021
Conditional Generative Quantile Networks via Optimal Transport and Convex Potentials
2021
Revisiting flow generative models for Out-of-distribution detection
2021
-Mutual Information Contrastive Learning
2021
Splitting Algorithms for Federated Learning
2021