17 citations · 24 across the 16 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…