activity
20132021
most citedLearning with Feature Evolvable Streams

32 citations · 99 across the 13 of their papers we have counts for

collaborators

26 papers

math.OC20214 cited

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…

cs.LG20212 cited

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…

cs.LG20212 cited

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…

cs.LG2021

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:…

cs.LG2020

Dynamic Regret of Convex and Smooth Functions

Peng Zhao, Yu-Jie Zhang, Lijun Zhang +1

We investigate online convex optimization in non-stationary environments and choose the dynamic regret as the performance measure, defined as the difference between cumulative loss…

cs.LG2020

Improved Analysis for Dynamic Regret of Strongly Convex and Smooth Functions

Peng Zhao, Lijun Zhang

In this paper, we present an improved analysis for dynamic regret of strongly convex and smooth functions. Specifically, we investigate the Online Multiple Gradient Descent (OMGD)…