ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1811.03179
  4. Cited By
How Well Generative Adversarial Networks Learn Distributions

How Well Generative Adversarial Networks Learn Distributions

7 November 2018
Tengyuan Liang
    GAN
ArXivPDFHTML

Papers citing "How Well Generative Adversarial Networks Learn Distributions"

24 / 24 papers shown
Title
Scalable Sobolev IPM for Probability Measures on a Graph
Scalable Sobolev IPM for Probability Measures on a Graph
Tam Le
Truyen V. Nguyen
H. Hino
Kenji Fukumizu
60
0
0
02 Feb 2025
Nested Annealed Training Scheme for Generative Adversarial Networks
Nested Annealed Training Scheme for Generative Adversarial Networks
Chang Wan
Ming-Hsuan Yang
Minglu Li
Yunliang Jiang
Zhonglong Zheng
GAN
43
0
0
20 Jan 2025
Theoretical Insights into CycleGAN: Analyzing Approximation and Estimation Errors in Unpaired Data Generation
Theoretical Insights into CycleGAN: Analyzing Approximation and Estimation Errors in Unpaired Data Generation
Luwei Sun
Dongrui Shen
Han Feng
43
2
0
16 Jul 2024
Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning
Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning
Yihong Gu
Cong Fang
Peter Bühlmann
Jianqing Fan
OOD
CML
77
2
0
07 May 2024
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically
  Low-dimensional Data
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data
Saptarshi Chakraborty
Peter L. Bartlett
44
1
0
24 Feb 2024
Statistically Optimal Generative Modeling with Maximum Deviation from
  the Empirical Distribution
Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution
Elen Vardanyan
Sona Hunanyan
T. Galstyan
A. Minasyan
A. Dalalyan
42
2
0
31 Jul 2023
Data Interpolants -- That's What Discriminators in Higher-order
  Gradient-regularized GANs Are
Data Interpolants -- That's What Discriminators in Higher-order Gradient-regularized GANs Are
Siddarth Asokan
C. Seelamantula
32
4
0
01 Jun 2023
Testing for the Markov Property in Time Series via Deep Conditional
  Generative Learning
Testing for the Markov Property in Time Series via Deep Conditional Generative Learning
Yunzhe Zhou
C. Shi
Lexin Li
Q. Yao
AI4TS
38
8
0
30 May 2023
Statistical Guarantees of Group-Invariant GANs
Statistical Guarantees of Group-Invariant GANs
Ziyu Chen
Markos A. Katsoulakis
Luc Rey-Bellet
Wei-wei Zhu
50
2
0
22 May 2023
Utility Theory of Synthetic Data Generation
Utility Theory of Synthetic Data Generation
Shi Xu
W. Sun
Guang Cheng
31
5
0
17 May 2023
Spider GAN: Leveraging Friendly Neighbors to Accelerate GAN Training
Spider GAN: Leveraging Friendly Neighbors to Accelerate GAN Training
Siddarth Asokan
C. Seelamantula
GAN
69
1
0
12 May 2023
Constrained Policy Optimization with Explicit Behavior Density for
  Offline Reinforcement Learning
Constrained Policy Optimization with Explicit Behavior Density for Offline Reinforcement Learning
Jing Zhang
Chi Zhang
Wenjia Wang
Bing-Yi Jing
OffRL
35
7
0
28 Jan 2023
Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics,
  Directional Convergence, and Equilibria
Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria
Tengyuan Liang
22
1
0
05 Dec 2022
Distribution estimation and change-point estimation for time series via
  DNN-based GANs
Distribution estimation and change-point estimation for time series via DNN-based GANs
Jianya Lu
Ying Mo
Zhijie Xiao
Lihu Xu
Qiuran Yao
AI4TS
36
0
0
26 Nov 2022
Asymptotic Statistical Analysis of $f$-divergence GAN
Asymptotic Statistical Analysis of fff-divergence GAN
Xinwei Shen
Kani Chen
Tong Zhang
33
2
0
14 Sep 2022
$α$-GAN: Convergence and Estimation Guarantees
ααα-GAN: Convergence and Estimation Guarantees
Gowtham R. Kurri
Monica Welfert
Tyler Sypherd
Lalitha Sankar
GAN
98
8
0
12 May 2022
Rates of convergence for nonparametric estimation of singular
  distributions using generative adversarial networks
Rates of convergence for nonparametric estimation of singular distributions using generative adversarial networks
Minwoo Chae
GAN
32
4
0
07 Feb 2022
Optimal 1-Wasserstein Distance for WGANs
Optimal 1-Wasserstein Distance for WGANs
Arthur Stéphanovitch
Ugo Tanielian
B. Cadre
N. Klutchnikoff
Gérard Biau
OT
GAN
20
3
0
08 Jan 2022
Reversible Gromov-Monge Sampler for Simulation-Based Inference
Reversible Gromov-Monge Sampler for Simulation-Based Inference
Y. Hur
Wenxuan Guo
Tengyuan Liang
36
9
0
28 Sep 2021
Wasserstein Generative Adversarial Uncertainty Quantification in
  Physics-Informed Neural Networks
Wasserstein Generative Adversarial Uncertainty Quantification in Physics-Informed Neural Networks
Yihang Gao
Michael K. Ng
38
29
0
30 Aug 2021
Realizing GANs via a Tunable Loss Function
Realizing GANs via a Tunable Loss Function
Gowtham R. Kurri
Tyler Sypherd
Lalitha Sankar
GAN
14
16
0
09 Jun 2021
Rates of convergence for density estimation with generative adversarial
  networks
Rates of convergence for density estimation with generative adversarial networks
Nikita Puchkin
S. Samsonov
Denis Belomestny
Eric Moulines
A. Naumov
37
10
0
30 Jan 2021
On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient
  Flow
On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient Flow
Youssef Mroueh
Truyen V. Nguyen
29
25
0
04 Nov 2020
A Review on Generative Adversarial Networks: Algorithms, Theory, and
  Applications
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Jie Gui
Zhenan Sun
Yonggang Wen
Dacheng Tao
Jieping Ye
EGVM
33
821
0
20 Jan 2020
1