15 citations · 52 across the 23 of their papers we have counts for
24 papers
Off-policy Evaluation in Doubly Inhomogeneous Environments
Zeyu Bian, Chengchun Shi, Zhengling Qi +1
This work aims to study off-policy evaluation (OPE) under scenarios where two key reinforcement learning (RL) assumptions -- temporal stationarity and individual homogeneity are bo…
A Policy Gradient Method for Confounded POMDPs
Mao Hong, Zhengling Qi, Yanxun Xu
In this paper, we propose a policy gradient method for confounded partially observable Markov decision processes (POMDPs) with continuous state and observation spaces in the offlin…
Sequential Knockoffs for Variable Selection in Reinforcement Learning
Tao Ma, Jin Zhu, Hengrui Cai +4
In real-world applications of reinforcement learning, it is often challenging to obtain a state representation that is parsimonious and satisfies the Markov property without prior…
Personalized Pricing with Invalid Instrumental Variables: Identification, Estimation, and Policy Learning
Rui Miao, Zhengling Qi, Cong Shi +1
Pricing based on individual customer characteristics is widely used to maximize sellers' revenues. This work studies offline personalized pricing under endogeneity using an instrum…
PASTA: Pessimistic Assortment Optimization
Juncheng Dong, Weibin Mo, Zhengling Qi +3
We consider a class of assortment optimization problems in an offline data-driven setting. A firm does not know the underlying customer choice model but has access to an offline da…
Value Enhancement of Reinforcement Learning via Efficient and Robust Trust Region Optimization
Chengchun Shi, Zhengling Qi, Jianing Wang +1
Reinforcement learning (RL) is a powerful machine learning technique that enables an intelligent agent to learn an optimal policy that maximizes the cumulative rewards in sequentia…
STEEL: Singularity-aware Reinforcement Learning
Xiaohong Chen, Zhengling Qi, Runzhe Wan
Batch reinforcement learning (RL) aims at leveraging pre-collected data to find an optimal policy that maximizes the expected total rewards in a dynamic environment. The existing m…
Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information
Zuyue Fu, Zhengling Qi, Zhuoran Yang +2
Motivated by the human-machine interaction such as training chatbots for improving customer satisfaction, we study human-guided human-machine interaction involving private informat…
RISE: Robust Individualized Decision Learning with Sensitive Variables
Xiaoqing Tan, Zhengling Qi, Christopher W. Seymour +1
This paper introduces RISE, a robust individualized decision learning framework with sensitive variables, where sensitive variables are collectible data and important to the interv…
Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Models
Rui Miao, Zhengling Qi, Xiaoke Zhang
We study the problem of off-policy evaluation (OPE) for episodic Partially Observable Markov Decision Processes (POMDPs) with continuous states. Motivated by the recently proposed…
Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach
Yunzhe Zhou, Zhengling Qi, Chengchun Shi +1
In this article, we propose a novel pessimism-based Bayesian learning method for optimal dynamic treatment regimes in the offline setting. When the coverage condition does not hold…
Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes
Zuyue Fu, Zhengling Qi, Zhaoran Wang +3
We study the offline reinforcement learning (RL) in the face of unmeasured confounders. Due to the lack of online interaction with the environment, offline RL is facing the followi…
Change Point Detection for High-dimensional Linear Models: A General Tail-adaptive Approach
Bin Liu, Zhengling Qi, Xinsheng Zhang +1
We propose a novel approach for detecting change points in high-dimensional linear regression models. Unlike previous research that relied on strict Gaussian/sub-Gaussian error ass…
On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation
Xiaohong Chen, Zhengling Qi
We study the off-policy evaluation (OPE) problem in an infinite-horizon Markov decision process with continuous states and actions. We recast the -function estimation into a spe…
Pessimistic Model Selection for Offline Deep Reinforcement Learning
Chao-Han Huck Yang, Zhengling Qi, Yifan Cui +1
Deep Reinforcement Learning (DRL) has demonstrated great potentials in solving sequential decision making problems in many applications. Despite its promising performance, practica…
Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules
Weibin Mo, Zhengling Qi, Yufeng Liu
We thank the opportunity offered by editors for this discussion and the discussants for their insightful comments and thoughtful contributions. We also want to congratulate Kallus…
Projected State-action Balancing Weights for Offline Reinforcement Learning
Jiayi Wang, Zhengling Qi, Raymond K. W. Wong
Offline policy evaluation (OPE) is considered a fundamental and challenging problem in reinforcement learning (RL). This paper focuses on the value estimation of a target policy ba…
Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding
Zhengling Qi, Rui Miao, Xiaoke Zhang
Data-driven individualized decision making has recently received increasing research interests. Most existing methods rely on the assumption of no unmeasured confounding, which unf…
Robust Batch Policy Learning in Markov Decision Processes
Zhengling Qi, Peng Liao
We study the offline data-driven sequential decision making problem in the framework of Markov decision process (MDP). In order to enhance the generalizability and adaptivity of th…
Batch Policy Learning in Average Reward Markov Decision Processes
Peng Liao, Zhengling Qi, Runzhe Wan +2
We consider the batch (off-line) policy learning problem in the infinite horizon Markov Decision Process. Motivated by mobile health applications, we focus on learning a policy tha…
Learning Optimal Distributionally Robust Individualized Treatment Rules
Weibin Mo, Zhengling Qi, Yufeng Liu
Recent development in the data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and o…
Statistical Analysis of Stationary Solutions of Coupled Nonconvex Nonsmooth Empirical Risk Minimization
Zhengling Qi, Ying Cui, Yufeng Liu +1
This paper has two main goals: (a) establish several statistical properties---consistency, asymptotic distributions, and convergence rates---of stationary solutions and values of a…
Estimation of Individualized Decision Rules Based on an Optimized Covariate-Dependent Equivalent of Random Outcomes
Zhengling Qi, Ying Cui, Yufeng Liu +1
Recent exploration of optimal individualized decision rules (IDRs) for patients in precision medicine has attracted a lot of attention due to the heterogeneous responses of patient…
On Robustness of Individualized Decision Rules
Zhengling Qi, Jong-Shi Pang, Yufeng Liu
With the emergence of precision medicine, estimating optimal individualized decision rules (IDRs) has attracted tremendous attention in many scientific areas. Most existing literat…