b'Chengchun Shi'

research

∙ 06/14/2023

Off-policy Evaluation in Doubly Inhomogeneous Environments

This work aims to study off-policy evaluation (OPE) under scenarios wher...

0 Zeyu Bian, et al. ∙

research

∙ 05/30/2023

Testing for the Markov Property in Time Series via Deep Conditional Generative Learning

The Markov property is widely imposed in analysis of time series data. C...

0 Yunzhe Zhou, et al. ∙

research

∙ 05/17/2023

Evaluating Dynamic Conditional Quantile Treatment Effects with Applications in Ridesharing

Many modern tech companies, such as Google, Uber, and Didi, utilize onli...

0 Ting Li, et al. ∙

research

∙ 03/24/2023

Sequential Knockoffs for Variable Selection in Reinforcement Learning

In real-world applications of reinforcement learning, it is often challe...

0 Tao Ma, et al. ∙

research

∙ 01/31/2023

A Reinforcement Learning Framework for Dynamic Mediation Analysis

Mediation analysis learns the causal effect transmitted via mediator var...

10 Lin Ge, et al. ∙

research

∙ 01/03/2023

Deep Spectral Q-learning with Application to Mobile Health

Dynamic treatment regimes assign personalized treatments to patients seq...

0 Yuhe Gao, et al. ∙

research

∙ 12/29/2022

An Instrumental Variable Approach to Confounded Off-Policy Evaluation

Off-policy evaluation (OPE) is a method for estimating the return of a t...

0 Yang Xu, et al. ∙

research

∙ 12/29/2022

Quantile Off-Policy Evaluation via Deep Conditional Generative Learning

Off-Policy evaluation (OPE) is concerned with evaluating a new target po...

0 Yang Xu, et al. ∙

research

∙ 12/13/2022

A Review of Off-Policy Evaluation in Reinforcement Learning

Reinforcement learning (RL) is one of the most vibrant research frontier...

0 Masatoshi Uehara, et al. ∙

research

∙ 11/08/2022

Doubly Inhomogeneous Reinforcement Learning

This paper studies reinforcement learning (RL) in doubly inhomogeneous e...

4 Liyuan Hu, et al. ∙

research

∙ 10/26/2022

Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach

In this article, we propose a novel pessimism-based Bayesian learning me...

0 Yunzhe Zhou, et al. ∙

research

∙ 09/29/2022

Blessing from Experts: Super Reinforcement Learning in Confounded Environments

We introduce super reinforcement learning in the batch setting, which ta...

10 Jiayi Wang, et al. ∙

research

∙ 09/23/2022

Sure Screening for Transelliptical Graphical Models

We propose a sure screening approach for recovering the structure of a t...

0 Yuxiang Xie, et al. ∙

research

∙ 07/26/2022

Future-Dependent Value-Based Off-Policy Evaluation in POMDPs

We study off-policy evaluation (OPE) for partially observable MDPs (POMD...

3 Masatoshi Uehara, et al. ∙

research

∙ 06/14/2022

Conformal Off-Policy Prediction

Off-policy evaluation is critical in a number of applications where new ...

0 Yingying Zhang, et al. ∙

research

∙ 03/03/2022

Reinforcement Learning in Possibly Nonstationary Environments

We consider reinforcement learning (RL) methods in offline nonstationary...

17 Mengbing Li, et al. ∙

research

∙ 02/26/2022

Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons

We consider reinforcement learning (RL) methods in offline domains witho...

0 Chengchun Shi, et al. ∙

research

∙ 02/22/2022

Policy Evaluation for Temporal and/or Spatial Dependent Experiments in Ride-sourcing Platforms

Policy evaluation based on A/B testing has attracted considerable intere...

0 Shikai Luo, et al. ∙

research

∙ 02/22/2022

Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process

This paper is concerned with constructing a confidence interval for a ta...

0 Chengchun Shi, et al. ∙

research

∙ 02/21/2022

A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets

The two-sided markets such as ride-sharing companies often involve a gro...

0 Chengchun Shi, et al. ∙

research

∙ 11/17/2021

Jump Interval-Learning for Individualized Decision Making

An individualized decision rule (IDR) is a decision function that assign...

0 Hengrui Cai, et al. ∙

research

∙ 11/12/2021

A Minimax Learning Approach to Off-Policy Evaluation in Partially Observable Markov Decision Processes

We consider off-policy evaluation (OPE) in Partially Observable Markov D...

0 Chengchun Shi, et al. ∙

research

∙ 11/06/2021

An Online Sequential Test for Qualitative Treatment Effects

Tech companies (e.g., Google or Facebook) often use randomized online ex...

0 Chengchun Shi, et al. ∙

research

∙ 06/02/2021

Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning

In this article, we propose a new hypothesis testing method for directed...

0 Chengchun Shi, et al. ∙

research

∙ 05/27/2021

Pattern Transfer Learning for Reinforcement Learning in Order Dispatching

Order dispatch is one of the central problems to ride-sharing platforms....

0 Runzhe Wan, et al. ∙

research

∙ 05/10/2021

Deeply-Debiased Off-Policy Interval Estimation

Off-policy evaluation learns a target policy's value with a historical d...

7 Chengchun Shi, et al. ∙

research

∙ 10/29/2020

Deep Jump Q-Evaluation for Offline Policy Evaluation in Continuous Action Space

We consider off-policy evaluation (OPE) in continuous action domains, su...

0 Hengrui Cai, et al. ∙

research

∙ 06/03/2020

Double Generative Adversarial Networks for Conditional Independence Testing

In this article, we consider the problem of high-dimensional conditional...

15 Chengchun Shi, et al. ∙

research

∙ 05/10/2020

Testing Mediation Effects Using Logic of Boolean Matrices

Mediation analysis is becoming an increasingly important tool in scienti...

0 Chengchun Shi, et al. ∙

research

∙ 02/05/2020

Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making

The Markov assumption (MA) is fundamental to the empirical validity of r...

0 Chengchun Shi, et al. ∙

research

∙ 02/05/2020

A Reinforcement Learning Framework for Time-Dependent Causal Effects Evaluation in A/B Testing

A/B testing, or online experiment is a standard business strategy to com...

0 Chengchun Shi, et al. ∙

research

∙ 10/15/2015

Robust Learning for Optimal Treatment Decision with NP-Dimensionality

In order to identify important variables that are involved in making opt...

0 Chengchun Shi, et al. ∙

Chengchun Shi

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