How Well Can Generative Adversarial Networks Learn Densities: A Nonparametric View

21 December 2017

Papers citing "How Well Can Generative Adversarial Networks Learn Densities: A Nonparametric View"

7 / 7 papers shown

Title
Scalable Sobolev IPM for Probability Measures on a Graph Tam Le Truyen V. Nguyen H. Hino Kenji Fukumizu 60 0 0 02 Feb 2025
Bounds on Lp errors in density ratio estimation via f-divergence loss functions Yoshiaki Kitazawa 26 0 0 02 Oct 2024
A Selective Overview of Deep Learning Jianqing Fan Cong Ma Yiqiao Zhong BDL VLM 38 136 0 10 Apr 2019
Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses Ananya Uppal Shashank Singh Barnabás Póczós 30 52 0 09 Feb 2019
Deep Neural Networks for Estimation and Inference M. Farrell Tengyuan Liang S. Misra BDL 27 254 0 26 Sep 2018
Nonparametric Density Estimation under Adversarial Losses Shashank Singh Ananya Uppal Boyue Li Chun-Liang Li Manzil Zaheer Barnabás Póczós GAN 29 55 0 22 May 2018
Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks Tengyuan Liang J. Stokes 32 211 0 16 Feb 2018