70 citations · 560 across the 53 of their papers we have counts for
8 papers · 1 filter
Momentum Accelerates the Convergence of Stochastic AUPRC Maximization
Guanghui Wang, Ming Yang, Lijun Zhang +1
In this paper, we study stochastic optimization of areas under precision-recall curves (AUPRC), which is widely used for combating imbalanced classification tasks. Although a few m…
Randomized Stochastic Variance-Reduced Methods for Multi-Task Stochastic Bilevel Optimization
Zhishuai Guo, Quanqi Hu, Lijun Zhang +1
In this paper, we consider non-convex stochastic bilevel optimization (SBO) problems that have many applications in machine learning. Although numerous studies have proposed stocha…
Universal Online Convex Optimization Meets Second-order Bounds
Lijun Zhang, Yibo Wang, Guanghui Wang +2
Recently, several universal methods have been proposed for online convex optimization, and attain minimax rates for multiple types of convex functions simultaneously. However, they…
Online Convex Optimization with Continuous Switching Constraint
Guanghui Wang, Yuanyu Wan, Tianbao Yang +1
In many sequential decision making applications, the change of decision would bring an additional cost, such as the wear-and-tear cost associated with changing server status. To co…
Online Strongly Convex Optimization with Unknown Delays
Yuanyu Wan, Wei-Wei Tu, Lijun Zhang
We investigate the problem of online convex optimization with unknown delays, in which the feedback of a decision arrives with an arbitrary delay. Previous studies have presented a…
Projection-free Distributed Online Learning with Sublinear Communication Complexity
Yuanyu Wan, Guanghui Wang, Wei-Wei Tu +1
To deal with complicated constraints via locally light computations in distributed online learning, a recent study has presented a projection-free algorithm called distributed onli…
A Simple Approach for Non-stationary Linear Bandits
Peng Zhao, Lijun Zhang, Yuan Jiang +1
This paper investigates the problem of non-stationary linear bandits, where the unknown regression parameter is evolving over time. Existing studies develop various algorithms and…
Revisiting Smoothed Online Learning
Lijun Zhang, Wei Jiang, Shiyin Lu +1
In this paper, we revisit the problem of smoothed online learning, in which the online learner suffers both a hitting cost and a switching cost, and target two performance metrics:…