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arXiv Machine Learning By Sumin Park, Taero Kim, Minhyeong Cho, Subeen Park, Jong Hwan Lee, Kyungwoo Song

IsoAct: Geometry-Aware Post-hoc Debiasing via Isometric Actions

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IsoAct is a post‑hoc debiasing framework that respects the geometry of non‑Euclidean representations. It identifies nuisance‑predictive directions via Fisher discriminant analysis in tangent coordinates and corrects them using manifold‑native actions that keep the representation on its manifold. Experiments on hyperspherical, hyperbolic, and SE(3) spaces show that IsoAct reduces nuisance recoverability while preserving utility and manifold validity, notably improving kinase‑group recoverability and structural prediction on the KLIFS dataset.

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