15 citations · 48 across the 19 of their papers we have counts for
8 papers · 1 filter
Zero-Inflated Bandits
Haoyu Wei, Runzhe Wan, Lei Shi +1
Many real-world bandit applications are characterized by sparse rewards, which can significantly hinder learning efficiency. Leveraging problem-specific structures for careful dist…
Effect Size Estimation for Duration Recommendation in Online Experiments: Leveraging Hierarchical Models and Objective Utility Approaches
Yu Liu, Runzhe Wan, James McQueen +3
The selection of the assumed effect size (AES) critically determines the duration of an experiment, and hence its accuracy and efficiency. Traditionally, experimenters determine AE…
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…
Experimentation Platforms Meet Reinforcement Learning: Bayesian Sequential Decision-Making for Continuous Monitoring
Runzhe Wan, Yu Liu, James McQueen +2
With the growing needs of online A/B testing to support the innovation in industry, the opportunity cost of running an experiment becomes non-negligible. Therefore, there is an inc…
Multiplier Bootstrap-based Exploration
Runzhe Wan, Haoyu Wei, Branislav Kveton +1
Despite the great interest in the bandit problem, designing efficient algorithms for complex models remains challenging, as there is typically no analytical way to quantify uncerta…
Heterogeneous Synthetic Learner for Panel Data
Ye Shen, Runzhe Wan, Hengrui Cai +1
In the new era of personalization, learning the heterogeneous treatment effect (HTE) becomes an inevitable trend with numerous applications. Yet, most existing HTE estimation metho…
Mining the Factor Zoo: Estimation of Latent Factor Models with Sufficient Proxies
Runzhe Wan, Yingying Li, Wenbin Lu +1
Latent factor model estimation typically relies on either using domain knowledge to manually pick several observed covariates as factor proxies, or purely conducting multivariate a…
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…