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This paper studies how well generative adversarial networks (GANs) learn probability distributions from finite samples.
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Approximability of discriminators implies diversity in GANs
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Nearly-tight VC-dimension and Pseudodimension bounds for piecewise linear neural networks
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Nonlinear approximation via compositions
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Nonparametric density estimation & convergence rates for GANs under Besov IPM losses
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