1 citations · 1 across the 4 of their papers we have counts for
10 papers
A Smoothed Analysis of Online Lasso for the Sparse Linear Contextual Bandit Problem
Zhiyuan Liu, Huazheng Wang, Bo Waggoner +3
We investigate the sparse linear contextual bandit problem where the parameter is sparse. To relieve the sampling inefficiency, we utilize the "perturbed adversary" where the c…
Multi-Level Optimal Power Flow Solver in Large Distribution Networks
Xinyang Zhou, Yue Chen, Zhiyuan Liu +2
Solving optimal power flow (OPF) problems for large distribution networks incurs high computational complexity. We consider a large multi-phase distribution network of tree topolog…
Utilizing Players' Playtime Records for Churn Prediction: Mining Playtime Regularity
Wanshan Yang, Ting Huang, Junlin Zeng +4
In the free online game industry, churn prediction is an important research topic. Reducing the churn rate of a game significantly helps with the success of the game. Churn predict…
Incentivized Exploration for Multi-Armed Bandits under Reward Drift
Zhiyuan Liu, Huazheng Wang, Fan Shen +2
We study incentivized exploration for the multi-armed bandit (MAB) problem where the players receive compensation for exploring arms other than the greedy choice and may provide bi…
Towards Scalable Koopman Operator Learning: Convergence Rates and A Distributed Learning Algorithm
Zhiyuan Liu, Guohui Ding, Lijun Chen +1
We propose an alternating optimization algorithm to the nonconvex Koopman operator learning problem for nonlinear dynamic systems. We show that the proposed algorithm will converge…
Solving Optimal Power Flow for Distribution Networks with State Estimation Feedback
Yi Guo, Xinyang Zhou, Changhong Zhao +3
Conventional optimal power flow (OPF) solvers assume full observability of the involved system states. However, in practice, there is a lack of reliable system monitoring devices i…