15 citations · 48 across the 19 of their papers we have counts for
9 papers · 1 filter
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…
Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework
Runzhe Wan, Lin Ge, Rui Song
Online learning in large-scale structured bandits is known to be challenging due to the curse of dimensionality. In this paper, we propose a unified meta-learning framework for a g…
Safe Exploration for Efficient Policy Evaluation and Comparison
Runzhe Wan, Branislav Kveton, Rui Song
High-quality data plays a central role in ensuring the accuracy of policy evaluation. This paper initiates the study of efficient and safe data collection for bandit policy evaluat…
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models
Runzhe Wan, Lin Ge, Rui Song
How to explore efficiently is a central problem in multi-armed bandits. In this paper, we introduce the metadata-based multi-task bandit problem, where the agent needs to solve a l…
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…
Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control
Runzhe Wan, Xinyu Zhang, Rui Song
Severe infectious diseases such as the novel coronavirus (COVID-19) pose a huge threat to public health. Stringent control measures, such as school closures and stay-at-home orders…