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Distribution Estimation of Contaminated Data via DNN-based MoM-GANs
This paper studies the distribution estimation of contaminated data by the MoM-GAN method, which combines generative adversarial net (GAN) and median-of-mean (MoM) estimation.
Distribution estimation and change-point estimation for time series via DNN-based GANs
The generative adversarial networks (GANs) have recently been applied to estimating the distribution of independent and identically distributed data, and have attracted a lot of research attention.
Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption
There has been a surge of interest in developing robust estimators for models with heavy-tailed and bounded variance data in statistics and machine learning, while few works impose unbounded variance.
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