Ming Yu
PhD (2016-2020)
Ming received his PhD in Econometrics and Statistics at University of Chicago, Booth School of Business in March 2020. His research interests include high dimensional statistical inference, non-convex optimization, and reinforcement learning, with a focus on developing novel methodologies with both practical applications and theoretical guarantees.
Interests
- Machine Learning
- High-dimensional statistical inference
- Nonconvex optimization
- Reinforcement learning
- Network models and information diffusion
Education
- PhD in Econometrics and Statistics, 2020University of Chicago Booth School of Business
- MS in Statistics, 2016University of Chicago
- BS in Mathematics, 2014Peking University
Publications
Estimation of a Low-rank Topic-Based Model for Information Cascades
Journal of Machine Learning Research
Recovery of simultaneous low rank and two-way sparse coefficient matrices, a nonconvex approach
Electronic Journal of Statistics, 14(1)
Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching
Journal of Machine Learning Research
Learning Influence-Receptivity Network Structure with Guarantee
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics
Convergent Policy Optimization for Safe Reinforcement Learning
Advances in Neural Information Processing Systems, 32
Provable Gaussian Embedding with One Observation
Advances in Neural Information Processing Systems, 31
An Influence-Receptivity Model for Topic based Information Cascades
2017 IEEE International Conference on Data Mining
Statistical Inference for Pairwise Graphical Models Using Score Matching
Advances in Neural Information Processing Systems, 29