Research map / Probabilistic and scientific modeling / Variational and generative sampling
A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add instance noise (data perturbation) with N(0, sigma~^2 I) to observations before sieve MLE | deep generative model sieve MLE · synthetic and real data (Section 5) | estimation performance | Helped |
| add instance noise (data perturbation) to data used with GAN estimator (ablation) | GAN · numerical studies (Section 5) | estimation performance | No clear effect |
| perturbation improves convergence rate only when sigma_* is at most order n^{-beta_*/(2(beta_*+t_*))} | deep generative model sieve MLE with data perturbation · theory (Wasserstein rate) | W1 convergence rate | Mixed |
| increase the noise level sigma_* (larger fixed noise) | deep generative model sieve MLE · theory (Hellinger rate, Corollary) | convergence rate of d_H | Mixed |
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