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Nonparametric Density Estimation under Adversarial Losses.

NeurIPS, (2018): 10225-10236

Cited by: 32|Views55
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Abstract

We study minimax convergence rates of nonparametric density estimation under a large class of loss functions called ``adversarial lossesu0027u0027, which, besides classical L^p losses, includes maximum mean discrepancy (MMD), Wasserstein distance, and total variation distance. These losses are closely related to the losses encoded by disc...More

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