17 citations · 24 across the 16 of their papers we have counts for
16 papers
Deep Distributional Learning with Non-crossing Quantile Network
Guohao Shen, Runpeng Dai, Guojun Wu +3
In this paper, we introduce a non-crossing quantile (NQ) network for conditional distribution learning. By leveraging non-negative activation functions, the NQ network ensures that…
Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal Interferences
Runpeng Dai, Jianing Wang, Fan Zhou +4
Off-policy evaluation (OPE) is widely applied in sectors such as pharmaceuticals and e-commerce to evaluate the efficacy of novel products or policies from offline datasets. This p…
Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data
Danyang Wang, Chengchun Shi, Shikai Luo +1
In real-world scenarios, datasets collected from randomized experiments are often constrained by size, due to limitations in time and budget. As a result, leveraging large observat…
Robust Offline Reinforcement learning with Heavy-Tailed Rewards
Jin Zhu, Runzhe Wan, Zhengling Qi +2
This paper endeavors to augment the robustness of offline reinforcement learning (RL) in scenarios laden with heavy-tailed rewards, a prevalent circumstance in real-world applicati…
Quantile Off-Policy Evaluation via Deep Conditional Generative Learning
Yang Xu, Chengchun Shi, Shikai Luo +2
Off-Policy evaluation (OPE) is concerned with evaluating a new target policy using offline data generated by a potentially different behavior policy. It is critical in a number of…
An Instrumental Variable Approach to Confounded Off-Policy Evaluation
Yang Xu, Jin Zhu, Chengchun Shi +2
Off-policy evaluation (OPE) is a method for estimating the return of a target policy using some pre-collected observational data generated by a potentially different behavior polic…
Conformal Off-policy Prediction
Yingying Zhang, Chengchun Shi, Shikai Luo
Off-policy evaluation is critical in a number of applications where new policies need to be evaluated offline before online deployment. Most existing methods focus on the expected…
Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons
Chengchun Shi, Shikai Luo, Yuan Le +2
We consider reinforcement learning (RL) methods in offline domains without additional online data collection, such as mobile health applications. Most of existing policy optimizati…
Policy Evaluation for Temporal and/or Spatial Dependent Experiments
Shikai Luo, Ying Yang, Chengchun Shi +3
The aim of this paper is to establish a causal link between the policies implemented by technology companies and the outcomes they yield within intricate temporal and/or spatial de…
Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process
Chengchun Shi, Jin Zhu, Ye Shen +3
This paper is concerned with constructing a confidence interval for a target policy's value offline based on a pre-collected observational data in infinite horizon settings. Most o…
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets
Chengchun Shi, Runzhe Wan, Ge Song +3
The two-sided markets such as ride-sharing companies often involve a group of subjects who are making sequential decisions across time and/or location. With the rapid development o…
An Online Sequential Test for Qualitative Treatment Effects
Chengchun Shi, Shikai Luo, Hongtu Zhu +1
Tech companies (e.g., Google or Facebook) often use randomized online experiments and/or A/B testing primarily based on the average treatment effects to compare their new product w…
Pattern Transfer Learning for Reinforcement Learning in Order Dispatching
Runzhe Wan, Sheng Zhang, Chengchun Shi +2
Order dispatch is one of the central problems to ride-sharing platforms. Recently, value-based reinforcement learning algorithms have shown promising performance on this problem. H…
Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems with Applications to Ride-sourcing Platforms
Fan Zhou, Shikai Luo, Xiaohu Qie +2
How to dynamically measure the local-to-global spatio-temporal coherence between demand and supply networks is a fundamental task for ride-sourcing platforms, such as DiDi. Such co…
Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework
Chengchun Shi, Xiaoyu Wang, Shikai Luo +3
A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries. Major chall…
Sure Screening for Gaussian Graphical Models
Shikai Luo, Rui Song, Daniela Witten
We propose {graphical sure screening}, or GRASS, a very simple and computationally-efficient screening procedure for recovering the structure of a Gaussian graphical model in the h…