Modern Techniques of Statistical Optimization for Machine Learning
Many problems in machine learning rely on statistics and optimization. To solve these problems, new techniques are needed. I will show some of these new techniques through some machine learning problems I have recently worked on, such as nonconvex stochastic optimization, distributed training, adversarial attack, generative model, etc.
Host: Rebecca Willett
Tong Zhang is a Professor of Computer Science and Mathematics at The Hong Kong University of Science and Technology. Previously, he was a professor at Rutgers University, and worked at IBM, Yahoo, Baidu, and Tencent. Tong Zhang's research interests include machine learning algorithms and theory, statistical methods for big data and their applications. He is a fellow of ASA and IMS, and he has been on the editorial boards of leading machine learning journals and program committees of top machine learning conferences. Tong Zhang received a B.A. in mathematics and computer science from Cornell University and a Ph.D. in Computer Science from Stanford University.