2.3k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Shuguang Cui5 profiles29 · h 63
- Arnulf Jentzen22 · h 49
- Z. Luo18 · h 89
- Guangxu Zhu5 profiles17 · h 33
- Xiaoguang Han2 profiles15 · h 42
- Hang Li2 profiles13 · h 18
- Tsung-Hui Chang11 · h 45
- Qingjiang Shi3 profiles9 · h 36
- H. Poor2 profiles7 · h 141
- Rui Zhang3 profiles7 · h 137
- Shiyin Li7 · h 21
- Xiaoyang Li2 profiles7 · h 9
- Chinese University of Hong Kong, ShenzhenCN158 papers
- University of MünsterDE21 papers
- Chinese University of Hong KongHK16 papers
- University of Hong KongHK15 papers
- ETH ZurichCH14 papers
- Sun Yat-sen UniversityCN13 papers
- National University of SingaporeSG12 papers
- Peng Cheng LaboratoryCN10 papers
- Princeton UniversityUS10 papers
- Tongji UniversityCN10 papers
- Beijing University of Posts and TelecommunicationsCN9 papers
- Chinese Academy of SciencesCN9 papers
5 papers · 2 filters
Distributed Stochastic Consensus Optimization with Momentum for Nonconvex Nonsmooth Problems
Zhiguo Wang, Jiawei Zhang, Tsung-Hui Chang +2
While many distributed optimization algorithms have been proposed for solving smooth or convex problems over the networks, few of them can handle non-convex and non-smooth problems…
Scalable Deep Reinforcement Learning for Ride-Hailing
Jiekun Feng, Mark Gluzman, J. G. Dai
Ride-hailing services, such as Didi Chuxing, Lyft, and Uber, arrange thousands of cars to meet ride requests throughout the day. We consider a Markov decision process (MDP) model o…
Queueing Network Controls via Deep Reinforcement Learning
J. G. Dai, Mark Gluzman
Novel advanced policy gradient (APG) methods, such as Trust Region policy optimization and Proximal policy optimization (PPO), have become the dominant reinforcement learning algor…
Demand Estimation from Sales Transaction Data -- Practical Extensions
Norbert Remenyi, Xiaodong Luo
In this paper we discuss practical limitations of the standard choice-based demand models used in the literature to estimate demand from sales transaction data. We present modifica…
Newton-Step-Based Hard Thresholding Algorithms for Sparse Signal Recovery
Nan Meng, Yun-Bin Zhao
Sparse signal recovery or compressed sensing can be formulated as certain sparse optimization problems. The classic optimization theory indicates that the Newton-like method often…