Wasserstein Filtering: A Sample Selection Method for Robust Distribution Learning
Given a dataset where a portion of the samples are contaminated, our goal is to recover the underlying clean population distribution. To this end, we propose 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. The core insight is to select a subset of samples whose empirical distribution maximizes its Wasserstein distance to the fully contaminated empirical distribution, thereby preferentially isolating and removing geometrically influential outlier
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- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Wasserstein Filtering: A Sample Selection Method for Robust ” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Yikai Xu →
“Wasserstein Filtering: A Sample Selection Method for Robust Distribution Learning”
- LinkedLinked via arxiv author · 85%Zizhao Chen →
“Wasserstein Filtering: A Sample Selection Method for Robust Distribution Learning”
- LinkedLinked via arxiv author · 85%Jian Huang →
“Wasserstein Filtering: A Sample Selection Method for Robust Distribution Learning”