activity
20192026
most citedRobust Bayesian Classification Using an Optimistic Score Ratio

3 citations · 10 across the 11 of their papers we have counts for

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Showing stat.MEShow all

6 papers · 1 filter

stat.ME2026

Causal Estimation of Share-Induced Engagement with Flywheel Effects

Weitao Cheng, Yilin Li, Yong Wang +1

Sustainable user growth in online platforms depends not only on acquiring new users but also on reactivating and engaging existing ones through social sharing features. A well-desi…

stat.ME2026

Experimentation for Different Scheduling Policies on Queues: Mixed Differences-in-Q Estimators Based on Little's Law

Nanshan Jia, Ramesh Johari, Nian Si +1

In data centers, tasks are dispatched to various servers to evenly distribute the workload. When a data center considers implementing a new scheduling algorithm, it typically condu…

stat.ME2025

Representation-Aware Distributionally Robust Optimization: A Knowledge Transfer Framework

Zitao Wang, Nian Si, Molei Liu

Distributionally robust optimization (DRO) protects statistical learning against distributional shifts by optimizing the worst-case performance over a set of perturbed distribution…

stat.ME2025

Experimental Designs for Multi-Item Multi-Period Inventory Control

Xinqi Chen, Xingyu Bai, Zeyu Zheng +1

Randomized experiments, or A/B testing, are the gold standard for evaluating interventions, yet they remain underutilized in inventory management. This study addresses this gap by…

stat.ME20242 cited

Seller-Side Experiments under Interference Induced by Feedback Loops in Two-Sided Platforms

Zhihua Zhu, Zheng Cai, Liang Zheng +1

Two-sided platforms are central to modern commerce and content sharing and often utilize A/B testing for developing new features. While user-side experiments are common, seller-sid…

stat.ME2023

Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach

Nian Si

In modern recommendation systems, the standard pipeline involves training machine learning models on historical data to predict user behaviors and improve recommendations continuou…