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Liang, Tengyuan [Author]

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

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[S.l.]: SSRN, 2020

Published in: University of Chicago, Becker Friedman Institute for Economics Working Paper ; No. 2020-153
Liang, Tengyuan [Author]

How Well Generative Adversarial Networks Learn Distributions

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[S.l.]: SSRN, [2021]

Published in: University of Chicago, Becker Friedman Institute for Economics Working Paper ; No. 2020-154
Liang, Tengyuan [Author]

Universal Prediction Band Via Semi-Definite Programming

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[S.l.]: SSRN, [2021]

Published in: University of Chicago, Becker Friedman Institute for Economics Working Paper ; No. 2021-41
Cong, Lin William [Author] ; Liang, Tengyuan [Other]; Zhang, Xiao [Other]

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

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[S.l.]: SSRN, [2019]
Liang, Tengyuan [Author]; Sur, Pragya [Author]

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

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[S.l.]: SSRN, 2020

Published in: University of Chicago, Becker Friedman Institute for Economics Working Paper ; No. 2020-152
Liang, Tengyuan [Author]; Tran-Bach, Hai [Author]

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

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[S.l.]: SSRN, [2021]

Published in: University of Chicago, Becker Friedman Institute for Economics Working Paper ; No. 2020-151
Farrell, Max H. [Author]; Liang, Tengyuan [Author]; Misra, Sanjog [Author]

Deep learning for individual heterogeneity : an automatic inference framework

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[London]: Cemmap, Centre for Microdata Methuods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [2021]

Published in: Centre for Microdata Methods and Practice: CEMMAP working papers ; 2021,29
Farrell, Max H. [Author]; Liang, Tengyuan [Author]; Misra, Sanjog [Author]

Deep learning for individual heterogeneity: An automatic inference framework

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London: Centre for Microdata Methods and Practice (cemmap), 2021
Liang, Tengyuan

Universal Prediction Band via Semi-Definite Programming

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Oxford University Press (OUP), 2022

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology, 84 (2022) 4, Seite 1558-1580
Liang, Tengyuan

Universal Prediction Band Via Semi-Definite Programming

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Elsevier BV, 2021

Published in: SSRN Electronic Journal (2021)
Liang, Tengyuan

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

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Elsevier BV, 2020

Published in: SSRN Electronic Journal (2020)
Liang, Tengyuan

How Well Generative Adversarial Networks Learn Distributions

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Elsevier BV, 2020

Published in: SSRN Electronic Journal (2020)
Liang, Tengyuan; Sur, Pragya

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

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Institute of Mathematical Statistics, 2022

Published in: The Annals of Statistics, 50 (2022) 3
Dou, Xialiang; Liang, Tengyuan

Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits

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Informa UK Limited, 2021

Published in: Journal of the American Statistical Association, 116 (2021) 535, Seite 1507-1520
Liang, Tengyuan; Rakhlin, Alexander

Just interpolate: Kernel “Ridgeless” regression can generalize

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Institute of Mathematical Statistics, 2020

Published in: The Annals of Statistics, 48 (2020) 3
Liang, Tengyuan; Sur, Pragya

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

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Elsevier BV, 2020

Published in: SSRN Electronic Journal (2020)
Hur, YoonHaeng; Liang, Tengyuan

Detecting Weak Distribution Shifts via Displacement Interpolation

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Informa UK Limited, 2024

Published in: Journal of Business & Economic Statistics (2024), Seite 1-77
Liang, Tengyuan; Tran-Bach, Hai

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

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Informa UK Limited, 2022

Published in: Journal of the American Statistical Association, 117 (2022) 539, Seite 1324-1337
Liang, Tengyuan; Tran-Bach, Hai

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

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Elsevier BV, 2020

Published in: SSRN Electronic Journal (2020)
Liang, Tengyuan; Su, Weijie J.

Statistical Inference for the Population Landscape via Moment-Adjusted Stochastic Gradients

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Oxford University Press (OUP), 2019

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology, 81 (2019) 2, Seite 431-456

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