18 citations · 35 across the 9 of their papers we have counts for
9 papers
How to Inverting the Leverage Score Distribution?
Zhihang Li, Zhao Song, Weixin Wang +2
Leverage score is a fundamental problem in machine learning and theoretical computer science. It has extensive applications in regression analysis, randomized algorithms, and neura…
Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources
Yikuan Li, Chengsheng Mao, Kaixuan Huang +4
Scarcity of health care resources could result in the unavoidable consequence of rationing. For example, ventilators are often limited in supply, especially during public health em…
Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability
Zhao Song, Yitan Wang, Zheng Yu +1
Sketching is one of the most fundamental tools in large-scale machine learning. It enables runtime and memory saving via randomly compressing the original large problem into lower…
Fast Graph Neural Tangent Kernel via Kronecker Sketching
Shunhua Jiang, Yunze Man, Zhao Song +2
Many deep learning tasks have to deal with graphs (e.g., protein structures, social networks, source code abstract syntax trees). Due to the importance of these tasks, people turne…
Fast Sketching of Polynomial Kernels of Polynomial Degree
Zhao Song, David P. Woodruff, Zheng Yu +1
Kernel methods are fundamental in machine learning, and faster algorithms for kernel approximation provide direct speedups for many core tasks in machine learning. The polynomial k…
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method
Junyu Zhang, Chengzhuo Ni, Zheng Yu +2
Policy gradient (PG) gives rise to a rich class of reinforcement learning (RL) methods. Recently, there has been an emerging trend to accelerate the existing PG methods such as REI…
Generalized Leverage Score Sampling for Neural Networks
Jason D. Lee, Ruoqi Shen, Zhao Song +2
Leverage score sampling is a powerful technique that originates from theoretical computer science, which can be used to speed up a large number of fundamental questions, e.g. linea…
Scalable Lattice Influence Maximization
Wei Chen, Ruihan Wu, Zheng Yu
Influence maximization is the task of finding k seed nodes in a social network such that the expected number of activated nodes in the network (under certain influence propagation…
Decentralized RLS with Data-Adaptive Censoring for Regressions over Large-Scale Networks
Zifeng Wang, Zheng Yu, Qing Ling +2
The deluge of networked data motivates the development of algorithms for computation- and communication-efficient information processing. In this context, three data-adaptive censo…