Tengyuan Liang

University of Chicago - Booth School of Business

SCHOLARLY PAPERS

7

DOWNLOADS
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Top 24,777

in Total Papers Downloads

3,973

SSRN CITATIONS

13

CROSSREF CITATIONS

3

Scholarly Papers (7)

1.

Textual Factors: A Scalable, Interpretable, and Data-Driven Approach to Analyzing Unstructured Information*

Number of pages: 58 Posted: 04 Jan 2019 Last Revised: 17 Jul 2024
Lin William Cong, Tengyuan Liang, Xiao Zhang and Wu Zhu
Cornell University - Samuel Curtis Johnson Graduate School of Management, University of Chicago - Booth School of Business, Compass Lexecon and Tsinghua University - School of Economics & Management
Downloads 2,638 (10,075)
Citation 11

Abstract:

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Big Data, Clustering, Factor Models, Interpretable AI, Language Models, Text Analytics

2.

Analyzing Textual Information at Scale

Number of pages: 34 Posted: 17 Sep 2019 Last Revised: 09 Dec 2019
Cornell University - Samuel Curtis Johnson Graduate School of Management, University of Chicago - Booth School of Business, Georgia State University and Compass Lexecon
Downloads 1,120 (37,354)
Citation 6

Abstract:

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Big Data, Machine Learning, Text-based Analysis, Topic Models, Unstructured Data, Word Embedding

3.

A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-L1-Norm Interpolated Classifiers

University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2020-152
Number of pages: 50 Posted: 19 Oct 2020
Tengyuan Liang and Pragya Sur
University of Chicago - Booth School of Business and affiliation not provided to SSRN
Downloads 64 (649,185)
Citation 8

Abstract:

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4.

Universal Prediction Band Via Semi-Definite Programming

University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2021-41
Number of pages: 16 Posted: 08 Apr 2021
Tengyuan Liang
University of Chicago - Booth School of Business
Downloads 48 (741,402)

Abstract:

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Uncertainty quantification, variance interpolation, nonparametric prediction band, semi-definite programming, sum-of-squares.

5.

Estimating Certain Integral Probability Metrics (IPMs) Is as Hard as Estimating under the IPMs

University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2020-153
Number of pages: 16 Posted: 20 Oct 2020
Tengyuan Liang
University of Chicago - Booth School of Business
Downloads 40 (796,941)

Abstract:

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6.

How Well Generative Adversarial Networks Learn Distributions

University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2020-154
Number of pages: 32 Posted: 19 Oct 2020
Tengyuan Liang
University of Chicago - Booth School of Business
Downloads 34 (843,533)
Citation 1

Abstract:

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Generative Adversarial Networks, Implicit Distribution Estimation, Simulated Method of Moments, Oracle Inequality, Neural Network Learning, Minimax Problem, Pair Regularization

7.

Mehler’s Formula, Branching Process, and Compositional Kernels of Deep Neural Networks

University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2020-151
Number of pages: 42 Posted: 19 Oct 2020
Tengyuan Liang and Hai Tran-Bach
University of Chicago - Booth School of Business and University of Chicago
Downloads 29 (886,099)

Abstract:

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