Search results
- Tong Zhang is a professor of Computer Science at University of Illinois Urbana-Champaign. Previously, he was a professor at the Hong Kong University of Science and Technology, Rutgers university, and worked at IBM, Yahoo, Google, Baidu, and Tencent.
tongzhang-ml.org/
People also ask
Who is Professor Tong Zhang?
Who is Zhang Hong?
Who is Prof Zhang?
Who was Zhang He?
What is Prof Zhang's research interest?
Tong Zhang is a professor of Computer Science at University of Illinois Urbana-Champaign . Previously, he was a professor at the Hong Kong University of Science and Technology, Rutgers university, and worked at IBM, Yahoo, Google, Baidu, and Tencent.
- Research
My research interests are machine learning and their...
- Publications
Selected papers from 2000 to present. , ",",, (editors),,,,...
- Book
Chapter 1: Introduction. Chapter 2: Basic Probability...
- Research
Articles 1–20. Chair Professor, The University of Hong Kong - Cited by 43,933 - Environmental Biotechnology - Environmental Microbiology - Environmental Microbiome - Biological...
Apr 26, 2024 · Professor Tong Zhang of the Department of Civil Engineering was elected Fellow of Hong Kong Academy of Engineering Sciences for recognition of his outstanding expertise and remarkable contributions to the field of engineering.
Nov 28, 2022 · December 9, 2022 is the 90th birthday of Tong Zhang, a mathematician in Institute of Mathematics, Chinese Academy of Sciences where he was always working on the Riemann problem for gas dynamics in his mathematical life.
- Jiequan Li
My research interests are machine learning and their applications. My Google scholar page can be found here. My research group investigates the fundamental theory of machine learning. Based on theoretical understanding, we also design efficient and effective machine learning algorithms.
Prof. Tong ZHANG, Chair Professor of Department of Computer Science and Engineering and Department of Mathematics, has been elected as IEEE Fellow for the Class of 2020. He is being recognized for his contributions to machine learning algorithms.
Chapter 1: Introduction. Chapter 2: Basic Probability Inequalities. Chapter 3: Uniform Convergence. Chapter 4: Empirical Covering Number Analysis and Symmetrization. Chapter 5: Covering Number Estimates. Chapter 6: Rademacher Complexity and Concentration Inequalities. Chapter 7: Algorithmic Stability Analysis. Chapter 8: Model Selection.