129 citations · 555 across the 28 of their papers we have counts for
15 papers · 1 filter
Learning from a Biased Sample
Roshni Sahoo, Lihua Lei, Stefan Wager
The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as those that will be faced when the decisi…
Improving knockoffs with conditional calibration
Yixiang Luo, William Fithian, Lihua Lei
The knockoff filter of Barber and Candes (arXiv:1404.5609) is a flexible framework for multiple testing in supervised learning models, based on introducing synthetic predictor vari…
Adaptive novelty detection with false discovery rate guarantee
Ariane Marandon, Lihua Lei, David Mary +1
This paper studies the semi-supervised novelty detection problem where a set of "typical" measurements is available to the researcher. Motivated by recent advances in multiple test…
Conformal Risk Control
Anastasios N. Angelopoulos, Stephen Bates, Adam Fisch +2
We extend conformal prediction to control the expected value of any monotone loss function. The algorithm generalizes split conformal prediction together with its coverage guarante…
BONuS: Multiple multivariate testing with a data-adaptivetest statistic
Chiao-Yu Yang, Lihua Lei, Nhat Ho +1
We propose a new adaptive empirical Bayes framework, the Bag-Of-Null-Statistics (BONuS) procedure, for multiple testing where each hypothesis testing problem is itself multivariate…
Testing for Outliers with Conformal p-values
Stephen Bates, Emmanuel Candès, Lihua Lei +2
This paper studies the construction of p-values for nonparametric outlier detection, taking a multiple-testing perspective. The goal is to test whether new independent samples belo…
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…
Conditional calibration for false discovery rate control under dependence
William Fithian, Lihua Lei
We introduce a new class of methods for finite-sample false discovery rate (FDR) control in multiple testing problems with dependent test statistics where the dependence is fully o…
Conformal Inference of Counterfactuals and Individual Treatment Effects
Lihua Lei, Emmanuel J. Candès
Evaluating treatment effect heterogeneity widely informs treatment decision making. At the moment, much emphasis is placed on the estimation of the conditional average treatment ef…
Smoothed Nested Testing on Directed Acyclic Graphs
Jackson H. Loper, Lihua Lei, William Fithian +1
We consider the problem of multiple hypothesis testing when there is a logical nested structure to the hypotheses. When one hypothesis is nested inside another, the outer hypothesi…
An Assumption-Free Exact Test For Fixed-Design Linear Models With Exchangeable Errors
Lihua Lei, Peter J. Bickel
We propose the Cyclic Permutation Test (CPT) to test general linear hypotheses for linear models. This test is non-randomized and valid in finite samples with exact Type I error $α…
Hierarchical community detection by recursive partitioning
Tianxi Li, Lihua Lei, Sharmodeep Bhattacharyya +4
The problem of community detection in networks is usually formulated as finding a single partition of the network into some "correct" number of communities. We argue that it is mor…
STAR: A general interactive framework for FDR control under structural constraints
Lihua Lei, Aaditya Ramdas, William Fithian
We propose a general framework based on selectively traversed accumulation rules (STAR) for interactive multiple testing with generic structural constraints on the rejection set. I…
AdaPT: An interactive procedure for multiple testing with side information
Lihua Lei, William Fithian
We consider the problem of multiple hypothesis testing with generic side information: for each hypothesis we observe both a p-value and some predictor encoding co…
Power of Ordered Hypothesis Testing
Lihua Lei, William Fithian
Ordered testing procedures are multiple testing procedures that exploit a pre-specified ordering of the null hypotheses, from most to least promising. We analyze and compare the po…