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 Sep 21, 2020

Fencing Off Silicon Valley: Cross-Border Venture Capital and Technology Spillovers

Ufuk Akcigit, Sina T. Ates, Josh Lerner, Richard R. Townsend, Yulia Zhestkova
Topics:  Technology & Innovation
BFI Working Paper Sep 18, 2020

COVID-19 Shifted Patent Applications toward Technologies that Support Working from Home

Nicholas Bloom, Steven J. Davis, Yulia Zhestkova
Topics:  COVID-19, Technology & Innovation
BFI Working Paper Jul 6, 2020

Technology Diffusion

Nancy Stokey
Topics:  Technology & Innovation
BFI Working Paper Jun 30, 2020

Is Attention Produced Rationally?

Erin T. Bronchetti, Judd B. Kessler, Ellen B. Magenheim, Dmitry Taubinsky, Eric Zwick
Topics:  Technology & Innovation