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Minimax Optimality (Probably) Doesn't Imply Distribution Learning for
  GANs

Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs

International Conference on Learning Representations (ICLR), 2022
18 January 2022
Sitan Chen
Jungshian Li
Yuanzhi Li
Raghu Meka
    GAN
ArXiv (abs)PDFHTMLGithub

Papers citing "Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs"

5 / 5 papers shown
Exploring Image Generation via Mutually Exclusive Probability Spaces and Local Correlation Hypothesis
Exploring Image Generation via Mutually Exclusive Probability Spaces and Local Correlation Hypothesis
Chenqiu Zhao
Anup Basu
305
0
0
26 Jun 2025
Provably learning a multi-head attention layer
Provably learning a multi-head attention layer
Sitan Chen
Yuanzhi Li
MLT
343
22
0
06 Feb 2024
Statistically Optimal Generative Modeling with Maximum Deviation from
  the Empirical Distribution
Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical DistributionInternational Conference on Machine Learning (ICML), 2023
Elen Vardanyan
Sona Hunanyan
T. Galstyan
A. Minasyan
A. Dalalyan
457
3
0
31 Jul 2023
Robust Estimation under the Wasserstein Distance
Robust Estimation under the Wasserstein Distance
Sloan Nietert
Rachel Cummings
Ziv Goldfeld
324
8
0
02 Feb 2023
Learning (Very) Simple Generative Models Is Hard
Learning (Very) Simple Generative Models Is HardNeural Information Processing Systems (NeurIPS), 2022
Sitan Chen
Jungshian Li
Yuanzhi Li
202
12
0
31 May 2022
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