Tengyuan Liang

About

Tengyuan Liang is the JP Gan Professor of Econometrics and Statistics, and Applied AI in the Wallman Society of Fellows at the University of Chicago Booth School of Business. His research builds mathematical foundations for modern AI, spanning statistical learning theory, generative models, and causal inference.

Research topics

• Statistics
• Applied mathematics
• Computer Science
• Mathematics
• Artificial Intelligence
• Algorithm
• Econometrics
• Combinatorics

Selected publications

• Just interpolate: Kernel “Ridgeless” regression can generalize

  The Annals of Statistics · 2020 · 216 citations

  1st authorCorresponding

  © Institute of Mathematical Statistics, 2020. In the absence of explicit regularization, Kernel “Ridgeless” Regression with nonlinear kernels has the potential to fit the training data perfectly. It has been observed empirically, however, that such interpolated solutions can still generalize well on test data. We isolate a phenomenon of implicit regularization for minimum-norm interpolated solutions which is due to a combination of high dimensionality of the input data, curvature of the kernel f…

  DOI

• A precise high-dimensional asymptotic theory for boosting and minimum-ℓ1-norm interpolated classifiers

  The Annals of Statistics · 2022-06-01 · 22 citations

  articleOpen access1st authorCorresponding

  This paper establishes a precise high-dimensional asymptotic theory for boosting on separable data, taking statistical and computational perspectives. We consider a high-dimensional setting where the number of features (weak learners) p scales with the sample size n, in an overparametrized regime. Under a class of statistical models, we provide an exact analysis of the generalization error of boosting when the algorithm interpolates the training data and maximizes the empirical ℓ1-margin. Furthe…

  PublisherOA PDFDOI

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

  SSRN Electronic Journal · 2024-01-01 · 5 citations

  articleOpen access
  PublisherDOI

• High-dimensional asymptotics of Langevin dynamics in spiked matrix models

  Information and Inference A Journal of the IMA · 2023-09-18 · 5 citations

  articleOpen access1st authorCorresponding

  Abstract We study Langevin dynamics for recovering the planted signal in the spiked matrix model. We provide a ‘path-wise’ characterization of the overlap between the output of the Langevin algorithm and the planted signal. This overlap is characterized in terms of a self-consistent system of integro-differential equations, usually referred to as the Crisanti–Horner–Sommers–Cugliandolo–Kurchan equations in the spin glass literature. As a second contribution, we derive an explicit formula for the…

  PublisherOA PDFDOI

• Randomization inference when N equals one

  Biometrika · 2025-01-01 · 3 citations

  article1st authorCorresponding

  Summary For decades, $ N $-of-1 experiments, where a unit serves as its own control and treatment in different time windows, have been used in certain medical contexts. However, due to effects that accumulate over long time windows and interventions that have complex evolution, a lack of robust inference tools has limited the widespread applicability of such $ N $-of-1 designs. This work combines techniques from experimental design in causal inference and system identification from control theor…

  PublisherDOI

Recent grants

• CAREER: New Statistical Paradigms Reconciling Empirical Surprises in Modern Machine Learning

  NSF · $400k · 2021–2026

Frequent coauthors

• Alexander Rakhlin

  32 shared

• Sanjog Misra

  University of Chicago

  22 shared

• Max Farrell

  University of California, Berkeley

  16 shared

• Tommaso Cai

  University of Oslo

  15 shared

• YoonHaeng Hur

  University of Chicago

  7 shared

• Hariharan Narayanan

  Tata Institute of Fundamental Research

  6 shared

• Max H. Farrell

  6 shared

• Karthik Sridharan

  5 shared

Awards & honors

• J. Parker Bursk Memorial Prize
• Winkelman Fellowship
• National Science Foundation CAREER Grant

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