March 22, 2024, 4:42 a.m. | Jose Blanchet, Jiajin Li, Markus Pelger, Greg Zanotti

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.14067v1 Announce Type: cross
Abstract: In this paper, we propose a novel conceptual framework to detect outliers using optimal transport with a concave cost function. Conventional outlier detection approaches typically use a two-stage procedure: first, outliers are detected and removed, and then estimation is performed on the cleaned data. However, this approach does not inform outlier removal with the estimation task, leaving room for improvement. To address this limitation, we propose an automatic outlier rectification mechanism that integrates rectification and …

abstract arxiv cost cs.lg data detection framework function however math.oc novel outlier outliers paper stage stat.me stat.ml transport type via

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