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…
Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits
Zixin Zhong, Wang Chi Cheung, Vincent Y. F. Tan
We study the Pareto frontier of two archetypal objectives in multi-armed bandits, namely, regret minimization (RM) and best arm identification (BAI) with a fixed horizon. It is fol…
Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions
Zixin Zhong, Wang Chi Cheung, Vincent Y. F. Tan
We consider a best arm identification (BAI) problem for stochastic bandits with adversarial corruptions in the fixed-budget setting of T steps. We design a novel randomized algorit…
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism
Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu
We consider un-discounted reinforcement learning (RL) in Markov decision processes (MDPs) under drifting non-stationarity, i.e., both the reward and state transition distributions…
Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting
Zixin Zhong, Wang Chi Cheung, Vincent Y. F. Tan
We design and analyze CascadeBAI, an algorithm for finding the best set of items, also called an arm, within the framework of cascading bandits. An upper bound on the time comp…
Non-Stationary Reinforcement Learning: The Blessing of (More) Optimism
Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu
We consider un-discounted reinforcement learning (RL) in Markov decision processes (MDPs) under temporal drifts, ie, both the reward and state transition distributions are allowed…
Exploration-Exploitation Trade-off in Reinforcement Learning on Online Markov Decision Processes with Global Concave Rewards
Wang Chi Cheung
We consider an agent who is involved in a Markov decision process and receives a vector of outcomes every round. Her objective is to maximize a global concave reward function on th…
Hedging the Drift: Learning to Optimize under Non-Stationarity
Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu
We introduce data-driven decision-making algorithms that achieve state-of-the-art \emph{dynamic regret} bounds for non-stationary bandit settings. These settings capture applicatio…
Inventory Balancing with Online Learning
Wang Chi Cheung, Will Ma, David Simchi-Levi +1
We study a general problem of allocating limited resources to heterogeneous customers over time under model uncertainty. Each type of customer can be serviced using different actio…
Learning to Optimize under Non-Stationarity
Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu
We introduce algorithms that achieve state-of-the-art \emph{dynamic regret} bounds for non-stationary linear stochastic bandit setting. It captures natural applications such as dyn…
Assortment Optimization under Unknown MultiNomial Logit Choice Models
Wang Chi Cheung, David Simchi-Levi
Motivated by e-commerce, we study the online assortment optimization problem. The seller offers an assortment, i.e. a subset of products, to each arriving customer, who then purcha…