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 outliers. To render this optimization computationally tractable, we introduce three algorithms: a margin...
| Authors | Yikai Xu, Zhao Chen, Jian Huang |
|---|---|
| Primary category | stat.ML |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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