48 citations · 141 across the 26 of their papers we have counts for
6 papers · 1 filter
Adaptive Step Sizes in Variance Reduction via Regularization
Bingcong Li, Georgios B. Giannakis
The main goal of this work is equipping convex and nonconvex problems with Barzilai-Borwein (BB) step size. With the adaptivity of BB step sizes granted, they can fail when the obj…
Nearly Optimal Algorithms for Piecewise-Stationary Cascading Bandits
Lingda Wang, Huozhi Zhou, Bingcong Li +2
Cascading bandit (CB) is a popular model for web search and online advertising, where an agent aims to learn the most attractive items out of a ground set of size during th…
A Multistep Lyapunov Approach for Finite-Time Analysis of Biased Stochastic Approximation
Gang Wang, Bingcong Li, Georgios B. Giannakis
Motivated by the widespread use of temporal-difference (TD-) and Q-learning algorithms in reinforcement learning, this paper studies a class of biased stochastic approximation (SA)…
Almost Tune-Free Variance Reduction
Bingcong Li, Lingda Wang, Georgios B. Giannakis
The variance reduction class of algorithms including the representative ones, SVRG and SARAH, have well documented merits for empirical risk minimization problems. However, they re…
On the Convergence of SARAH and Beyond
Bingcong Li, Meng Ma, Georgios B. Giannakis
The main theme of this work is a unifying algorithm, \textbf{L}oop\textbf{L}ess \textbf{S}ARAH (L2S) for problems formulated as summation of individual loss functions. L2S broa…
Tight Linear Convergence Rate of ADMM for Decentralized Optimization
Meng Ma, Bingcong Li, Georgios B. Giannakis
The present paper considers leveraging network topology information to improve the convergence rate of ADMM for decentralized optimization, where networked nodes work collaborative…