activity
20192026
most citedKnockoffs with Side Information

7 citations · 14 across the 4 of their papers we have counts for

collaborators

7 papers

stat.ML2026

Learning to target with network interference

Xiaomeng Wang, Hamsa Bastani, Osbert Bastani +1

This paper studies adaptive targeting under network interference in a bandit setting, where treatments applied to one individual may affect others through spillover effects. We con…

cs.LG2024

Distributionally Robust Policy Learning under Concept Drifts

Jingyuan Wang, Zhimei Ren, Ruohan Zhan +1

Distributionally robust policy learning aims to find a policy that performs well under the worst-case distributional shift, and yet most existing methods for robust policy learning…

stat.AP20211 cited

Transfer learning in genome-wide association studies with knockoffs

Shuangning Li, Zhimei Ren, Chiara Sabatti +1

This paper presents and compares alternative transfer learning methods that can increase the power of conditional testing via knockoffs by leveraging prior information in external…

cs.LG2021

Online Multi-Armed Bandits with Adaptive Inference

Maria Dimakopoulou, Zhimei Ren, Zhengyuan Zhou

During online decision making in Multi-Armed Bandits (MAB), one needs to conduct inference on the true mean reward of each arm based on data collected so far at each step. However,…

stat.ME20206 cited

Derandomizing Knockoffs

Zhimei Ren, Yuting Wei, Emmanuel Candès

Model-X knockoffs is a general procedure that can leverage any feature importance measure to produce a variable selection algorithm, which discovers true effects while rigorously c…

stat.ME20207 cited

Knockoffs with Side Information

Zhimei Ren, Emmanuel Candès

We consider the problem of assessing the importance of multiple variables or factors from a dataset when side information is available. In principle, using side information can all…