41 citations · 86 across the 15 of their papers we have counts for
16 papers
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…
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…
Iterative Approximate Cross-Validation
Yuetian Luo, Zhimei Ren, Rina Foygel Barber
Cross-validation (CV) is one of the most popular tools for assessing and selecting predictive models. However, standard CV suffers from high computational cost when the number of f…
Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequality
Ying Jin, Zhimei Ren, Zhuoran Yang +1
This paper studies offline policy learning, which aims at utilizing observations collected a priori (from either fixed or adaptively evolving behavior policies) to learn an optimal…
Conformalized survival analysis with adaptive cutoffs
Yu Gui, Rohan Hore, Zhimei Ren +1
This paper introduces an assumption-lean method that constructs valid and efficient lower predictive bounds (LPBs) for survival times with censored data. We build on recent work by…
Derandomized knockoffs: leveraging e-values for false discovery rate control
Zhimei Ren, Rina Foygel Barber
Model-X knockoffs is a flexible wrapper method for high-dimensional regression algorithms, which provides guaranteed control of the false discovery rate (FDR). Due to the randomnes…
Sensitivity analysis under the -sensitivity models: a distributional robustness perspective
Ying Jin, Zhimei Ren, Zhengyuan Zhou
This paper introduces the -sensitivity model, a new sensitivity model that characterizes the violation of unconfoundedness in causal inference. It assumes the selection bias due…
Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approach
Ying Jin, Zhimei Ren, Emmanuel J. Candès
We propose a model-free framework for sensitivity analysis of individual treatment effects (ITEs), building upon ideas from conformal inference. For any unit, our procedure reports…
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…
Policy Learning with Adaptively Collected Data
Ruohan Zhan, Zhimei Ren, Susan Athey +1
Learning optimal policies from historical data enables personalization in a wide variety of applications including healthcare, digital recommendations, and online education. The gr…
Conformalized Survival Analysis
Emmanuel J. Candès, Lihua Lei, Zhimei Ren
Existing survival analysis techniques heavily rely on strong modelling assumptions and are, therefore, prone to model misspecification errors. In this paper, we develop an inferent…
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,…
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…
Dynamic Batch Learning in High-Dimensional Sparse Linear Contextual Bandits
Zhimei Ren, Zhengyuan Zhou
We study the problem of dynamic batch learning in high-dimensional sparse linear contextual bandits, where a decision maker, under a given maximum-number-of-batch constraint and on…
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…
Batched Multi-armed Bandits Problem
Zijun Gao, Yanjun Han, Zhimei Ren +1
In this paper, we study the multi-armed bandit problem in the batched setting where the employed policy must split data into a small number of batches. While the minimax regret for…