41 citations · 86 across the 15 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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…