BFI Working Paper Oct 16, 2020

How Well Generative Adversarial Networks Learn Distributions

Tengyuan Liang
Topics:  Technology & Innovation
BFI Working Paper Oct 16, 2020

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

Tengyuan Liang
Topics:  Technology & Innovation
BFI Working Paper Oct 16, 2020

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

Tengyuan Liang, Pragya Sur
Topics:  Technology & Innovation
BFI Working Paper Oct 16, 2020

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

Tengyuan Liang, Hai Tran-Bach
Topics:  Technology & Innovation
BFI Working Paper Oct 5, 2020

An Adversarial Approach to Structural Estimation

Tetsuya Kaji, Elena Manresa, Guillaume Pouliot
Topics:  Economic Mobility & Poverty
BFI Working Paper Sep 18, 2020

Inference for Large-Scale Linear Systems with Known Coefficients

Zheng Fang, Andres Santos, Azeem Shaikh, Alexander Torgovitsky
BFI Working Paper Jul 16, 2020

Inference with Imperfect Randomization: The Case of the Perry Preschool Program

James Heckman, Rodrigo Pinto, Azeem Shaikh
Topics:  Early Childhood Education
BFI Working Paper Mar 19, 2020

Inference for Ranks with Applications to Mobility across Neighborhoods and Academic Achievement across Countries

Magne Mogstad, Joseph P. Romano, Azeem Shaikh, Daniel Wilhelm