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researcher

Jing Lei

11 papers hereh-index 347.3k citations70 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author3
  • first author2
  • middle author5
  • last author1

Across the 11 of 11 papers where every author was matched, so the position is known.

fields
  • stat.ME4
  • stat.ML3
  • math.ST2
  • cs.LG1
  • math.PR1
same name
  • Jing Lei — 9 papers
  • Jing Lei — 4 papers, h 14
  • Jing Lei — 4 papers, h 1
  • Jing Lei — 2 papers, h 1
  • Jing Lei — 2 papers, h 4
  • Jing Lei — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20132025
most citedFast Exact Conformalization of Lasso using Piecewise Linear Homotopy

3 citations · 3 across the 4 of their papers we have counts for

collaborators
Showing stat.MEShow all

4 papers · 1 filter

stat.ME2022

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…

stat.ME2019

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…

stat.ME2018

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…

stat.ME2017★ 3 cited

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.