Wasserstein Filtering: A Sample Selection Method for Robust Distribution Learning
This work proposes Wasserstein Filtering (WF), a novel sample selection framework that discards a fraction of suspicious samples and estimates the target distribution using the empirical measure of the remaining data, and proves that the WF estimator achieves minimax optimality over distribution families with bounded c...