331 citations · 1.3k across the 39 of their papers we have counts for
40 papers
StablePCA: Distributionally Robust Learning of Shared Representations from Multi-Source Data
Zhenyu Wang, Molei Liu, Jing Lei +2
When synthesizing multi-source high-dimensional data, a key objective is to extract low-dimensional representations that effectively approximate the original features across differ…
Unbiased Test Error Estimation in the Poisson Means Problem via Coupled Bootstrap Techniques
Natalia L. Oliveira, Jing Lei, Ryan J. Tibshirani
We propose a coupled bootstrap (CB) method for the test error of an arbitrary algorithm that estimates the mean in a Poisson sequence, often called the Poisson means problem. The i…
Black-Box Model Confidence Sets Using Cross-Validation with High-Dimensional Gaussian Comparison
Nicholas Kissel, Jing Lei
We derive high-dimensional Gaussian comparison results for the standard -fold cross-validated risk estimates. Our results combine a recent stability-based argument for the low-d…
From local to global gene co-expression estimation using single-cell RNA-seq data
Jinjin Tian, Jing Lei, Kathryn Roeder
In genomics studies, the investigation of the gene relationship often brings important biological insights. Currently, the large heterogeneous datasets impose new challenges for st…
Unbiased Risk Estimation in the Normal Means Problem via Coupled Bootstrap Techniques
Natalia L. Oliveira, Jing Lei, Ryan J. Tibshirani
We develop a new approach for estimating the risk of an arbitrary estimator of the mean vector in the classical normal means problem. The key idea is to generate two auxiliary data…
Asymptotic Distribution-Free Independence Test for High Dimension Data
Zhanrui Cai, Jing Lei, Kathryn Roeder
Test of independence is of fundamental importance in modern data analysis, with broad applications in variable selection, graphical models, and causal inference. When the data is h…
Model-Independent Detection of New Physics Signals Using Interpretable Semi-Supervised Classifier Tests
Purvasha Chakravarti, Mikael Kuusela, Jing Lei +1
A central goal in experimental high energy physics is to detect new physics signals that are not explained by known physics. In this paper, we aim to search for new signals that ap…
A Two-Sample Conditional Distribution Test Using Conformal Prediction and Weighted Rank Sum
Xiaoyu Hu, Jing Lei
We consider the problem of testing the equality of conditional distributions of a response variable given a vector of covariates between two populations. Such a hypothesis testing…
Bias-adjusted spectral clustering in multi-layer stochastic block models
Jing Lei, Kevin Z. Lin
We consider the problem of estimating common community structures in multi-layer stochastic block models, where each single layer may not have sufficient signal strength to recover…
Gradient-based Sparse Principal Component Analysis with Extensions to Online Learning
Yixuan Qiu, Jing Lei, Kathryn Roeder
Sparse principal component analysis (PCA) is an important technique for dimensionality reduction of high-dimensional data. However, most existing sparse PCA algorithms are based on…
Global and Local Two-Sample Tests via Regression
Ilmun Kim, Ann B. Lee, Jing Lei
Two-sample testing is a fundamental problem in statistics. Despite its long history, there has been renewed interest in this problem with the advent of high-dimensional and complex…
On the probability that two random integers are coprime
Jing Lei, Joseph B. Kadane
We show that there is a non-empty class of finitely additive probabilities on such that for each member of the class, each set with limiting relative frequency ha…
Convergence and Concentration of Empirical Measures under Wasserstein Distance in Unbounded Functional Spaces
Jing Lei
We provide upper bounds of the expected Wasserstein distance between a probability measure and its empirical version, generalizing recent results for finite dimensional Euclidean s…
Network Representation Using Graph Root Distributions
Jing Lei
Exchangeable random graphs serve as an important probabilistic framework for the statistical analysis of network data. In this work we develop an alternative parameterization for a…
Fast Exact Conformalization of Lasso using Piecewise Linear Homotopy
Jing Lei
Conformal prediction is a general method that converts almost any point predictor to a prediction set. The resulting set keeps good statistical properties of the original estimator…
Cross-Validation with Confidence
Jing Lei
Cross-validation is one of the most popular model selection methods in statistics and machine learning. Despite its wide applicability, traditional cross validation methods tend to…
Bootstrapping and Sample Splitting For High-Dimensional, Assumption-Free Inference
Alessandro Rinaldo, Larry Wasserman, Max G'Sell +1
Several new methods have been proposed for performing valid inference after model selection. An older method is sampling splitting: use part of the data for model selection and par…
A Unified Statistical Framework for Single Cell and Bulk RNA Sequencing Data
Lingxue Zhu, Jing Lei, Bernie Devlin +1
Recent advances in technology have enabled the measurement of RNA levels for individual cells. Compared to traditional tissue-level bulk RNA-seq data, single cell sequencing yields…
Least Ambiguous Set-Valued Classifiers with Bounded Error Levels
Mauricio Sadinle, Jing Lei, Larry Wasserman
In most classification tasks there are observations that are ambiguous and therefore difficult to correctly label. Set-valued classifiers output sets of plausible labels rather tha…
Differentially Private Model Selection with Penalized and Constrained Likelihood
Jing Lei, Anne-Sophie Charest, Aleksandra Slavkovic +2
In statistical disclosure control, the goal of data analysis is twofold: The released information must provide accurate and useful statistics about the underlying population of int…
Testing High Dimensional Covariance Matrices, with Application to Detecting Schizophrenia Risk Genes
Lingxue Zhu, Jing Lei, Bernie Devlin +1
Scientists routinely compare gene expression levels in cases versus controls in part to determine genes associated with a disease. Similarly, detecting case-control differences in…
On-Average KL-Privacy and its equivalence to Generalization for Max-Entropy Mechanisms
Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg
We define On-Average KL-Privacy and present its properties and connections to differential privacy, generalization and information-theoretic quantities including max-information an…
Distribution-Free Predictive Inference For Regression
Jing Lei, Max G'Sell, Alessandro Rinaldo +2
We develop a general framework for distribution-free predictive inference in regression, using conformal inference. The proposed methodology allows for the construction of a predic…
A Minimax Theory for Adaptive Data Analysis
Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg
In adaptive data analysis, the user makes a sequence of queries on the data, where at each step the choice of query may depend on the results in previous steps. The releases are of…