30 citations · 65 across the 7 of their papers we have counts for
10 papers
An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods
Yanli Liu, Kaiqing Zhang, Tamer Başar +1
In this paper, we revisit and improve the convergence of policy gradient (PG), natural PG (NPG) methods, and their variance-reduced variants, under general smooth policy parametriz…
SMOF: Squeezing More Out of Filters Yields Hardware-Friendly CNN Pruning
Yanli Liu, Bochen Guan, Qinwen Xu +2
For many years, the family of convolutional neural networks (CNNs) has been a workhorse in deep learning. Recently, many novel CNN structures have been designed to address increasi…
An Improved Analysis of Stochastic Gradient Descent with Momentum
Yanli Liu, Yuan Gao, Wotao Yin
SGD with momentum (SGDM) has been widely applied in many machine learning tasks, and it is often applied with dynamic stepsizes and momentum weights tuned in a stagewise manner. De…
Decentralized Learning with Lazy and Approximate Dual Gradients
Yanli Liu, Yuejiao Sun, Wotao Yin
This paper develops algorithms for decentralized machine learning over a network, where data are distributed, computation is localized, and communication is restricted between neig…
Acceleration of SVRG and Katyusha X by Inexact Preconditioning
Yanli Liu, Fei Feng, Wotao Yin
Empirical risk minimization is an important class of optimization problems with many popular machine learning applications, and stochastic variance reduction methods are popular ch…
Acceleration of Primal-Dual Methods by Preconditioning and Simple Subproblem Procedures
Yanli Liu, Yunbei Xu, Wotao Yin
Primal-Dual Hybrid Gradient (PDHG) and Alternating Direction Method of Multipliers (ADMM) are two widely-used first-order optimization methods. They reduce a difficult problem to s…