70 citations · 99 across the 7 of their papers we have counts for
8 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…
Non-stationary Projection-free Online Learning with Dynamic and Adaptive Regret Guarantees
Yibo Wang, Wenhao Yang, Wei Jiang +5
Projection-free online learning has drawn increasing interest due to its efficiency in solving high-dimensional problems with complicated constraints. However, most existing projec…
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:…
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…
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…
Adaptivity and Optimality: A Universal Algorithm for Online Convex Optimization
Guanghui Wang, Shiyin Lu, Lijun Zhang
In this paper, we study adaptive online convex optimization, and aim to design a universal algorithm that achieves optimal regret bounds for multiple common types of loss functions…
Adaptive Online Learning in Dynamic Environments
Lijun Zhang, Shiyin Lu, Zhi-Hua Zhou
In this paper, we study online convex optimization in dynamic environments, and aim to bound the dynamic regret with respect to any sequence of comparators. Existing work have show…