70 citations · 560 across the 53 of their papers we have counts for
10 papers · 1 filter
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…