7 papers
Full-conformal novelty detection
Junu Lee, Ilia Popov, Zhimei Ren
This paper presents a powerful methodology for flexible full-data nonparametric novelty detection that offers distribution-free false discovery rate (FDR) control guarantees. Build…
Assumption-lean weak limits and tests for two-stage adaptive experiments
Ziang Niu, Zhimei Ren
Adaptive experiments are becoming increasingly popular in real-world applications for effectively maximizing in-sample welfare and efficiency by data-driven sampling. Despite their…
Optimal training-conditional regret for online conformal prediction
Jiadong Liang, Zhimei Ren, Yuxin Chen
We study online conformal prediction for non-stationary data streams subject to unknown distribution drift. While most prior work studied this problem under adversarial settings an…
Conditional predictive inference with -coverage control
Yonghoon Lee, Zhimei Ren
We consider the problem of distribution-free conditional predictive inference. Prior work has established that achieving exact finite-sample control of conditional coverage without…
Not all distributional shifts are equal: Fine-grained robust conformal inference
Jiahao Ai, Zhimei Ren
We introduce a fine-grained framework for uncertainty quantification of predictive models under distributional shifts. This framework distinguishes the shift in covariate distribut…
One-at-a-time knockoffs: controlled false discovery rate with higher power
Charlie K. Guan, Zhimei Ren, Daniel W. Apley
We propose one-at-a-time knockoffs (OATK), a new methodology for detecting important explanatory variables in linear regression models while controlling the false discovery rate (F…