33 citations · 106 across the 15 of their papers we have counts for
16 papers
Pure Exploration for a Good Policy in Reinforcement Learning with Bandit Feedback
Zitian Li, Wang Chi Cheung
Pure exploration in episodic Reinforcement Learning has primarily focused on Best Policy Identification (BPI), which seeks to identify a (near)-optimal policy with high confidence.…
Closing the Gap on the Sample Complexity of 1-Identification
Zitian Li, Wang Chi Cheung
The 1-identification problem is a fundamental pure-exploration problem in multi-armed bandits. An agent aims to determine whether there exists an arm whose mean reward exceeds a kn…
Online Bandits with (Biased) Offline Data: Adaptive Learning under Distribution Mismatch
Wang Chi Cheung, Lixing Lyu
Traditional online learning models are typically initialized from scratch. By contrast, contemporary real-world applications often have access to historical datasets that can poten…
Best Arm Identification with Resource Constraints
Zitian Li, Wang Chi Cheung
Motivated by the cost heterogeneity in experimentation across different alternatives, we study the Best Arm Identification with Resource Constraints (BAIwRC) problem. The agent aim…
Online Resource Allocation: Bandits feedback and Advice on Time-varying Demands
Lixing Lyu, Wang Chi Cheung
We consider a general online resource allocation model with bandit feedback and time-varying demands. While online resource allocation has been well studied in the literature, most…
Online Resource Allocation for Reusable Resources
Xilin Zhang, Wang Chi Cheung
We study a general model on reusable resource allocation under model uncertainty. A heterogeneous population of customers arrive at the decision maker's (DM's) platform sequentiall…