70 citations · 560 across the 53 of their papers we have counts for
54 papers
Revisiting Smoothed Online Learning: Competitive Ratio, Dynamic Regret with Switching Cost, and Best of Both Worlds
Lijun Zhang, Linzhe He, Wei Jiang +1
We revisit smoothed online learning and develop algorithms with improved competitive ratios, optimal dynamic regret with switching cost, and best-of-both-worlds guarantees. Surpris…
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:…
Projection-free Online Learning over Strongly Convex Sets
Yuanyu Wan, Lijun Zhang
To efficiently solve online problems with complicated constraints, projection-free algorithms including online frank-wolfe (OFW) and its variants have received significant interest…
Approximate Multiplication of Sparse Matrices with Limited Space
Yuanyu Wan, Lijun Zhang
Approximate matrix multiplication with limited space has received ever-increasing attention due to the emergence of large-scale applications. Recently, based on a popular matrix sk…
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…
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)…
Minimizing Dynamic Regret and Adaptive Regret Simultaneously
Lijun Zhang, Shiyin Lu, Tianbao Yang
Regret minimization is treated as the golden rule in the traditional study of online learning. However, regret minimization algorithms tend to converge to the static optimum, thus…
Adaptive and Efficient Algorithms for Tracking the Best Expert
Shiyin Lu, Lijun Zhang
In this paper, we consider the problem of prediction with expert advice in dynamic environments. We choose tracking regret as the performance metric and develop two adaptive and ef…
Stochastic Optimization for Non-convex Inf-Projection Problems
Yan Yan, Yi Xu, Lijun Zhang +2
In this paper, we study a family of non-convex and possibly non-smooth inf-projection minimization problems, where the target objective function is equal to minimization of a joint…
Bandit Convex Optimization in Non-stationary Environments
Peng Zhao, Guanghui Wang, Lijun Zhang +1
Bandit Convex Optimization (BCO) is a fundamental framework for modeling sequential decision-making with partial information, where the only feedback available to the player is the…
Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions
Lijun Zhang, Guanghui Wang, Wei-Wei Tu +1
To deal with changing environments, a new performance measure -- adaptive regret, defined as the maximum static regret over any interval, was proposed in online learning. Under the…
Multi-Objective Generalized Linear Bandits
Shiyin Lu, Guanghui Wang, Yao Hu +1
In this paper, we study the multi-objective bandits (MOB) problem, where a learner repeatedly selects one arm to play and then receives a reward vector consisting of multiple objec…
Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss
Pengcheng Li, Jinfeng Yi, Bowen Zhou +1
Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial examples. In this paper, we improve the robustness of DNNs by utilizing techniques of…
Prediction with Unpredictable Feature Evolution
Bo-Jian Hou, Lijun Zhang, Zhi-Hua Zhou
Learning with feature evolution studies the scenario where the features of the data streams can evolve, i.e., old features vanish and new features emerge. Its goal is to keep the m…
Adaptive Regret of Convex and Smooth Functions
Lijun Zhang, Tie-Yan Liu, Zhi-Hua Zhou
We investigate online convex optimization in changing environments, and choose the adaptive regret as the performance measure. The goal is to achieve a small regret over every inte…
Stochastic Primal-Dual Algorithms with Faster Convergence than for Problems without Bilinear Structure
Yan Yan, Yi Xu, Qihang Lin +2
Previous studies on stochastic primal-dual algorithms for solving min-max problems with faster convergence heavily rely on the bilinear structure of the problem, which restricts th…
Stochastic Approximation of Smooth and Strongly Convex Functions: Beyond the Convergence Rate
Lijun Zhang, Zhi-Hua Zhou
Stochastic approximation (SA) is a classical approach for stochastic convex optimization. Previous studies have demonstrated that the convergence rate of SA can be improved by intr…