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arXiv Machine Learning By Yingjie Bai, Weiming Zhi

When Does Data Value Reduce to Class Balance? A Coverage View of Per-Point Data Valuation

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The paper investigates when per‑point data‑valuation scores (e.g., Data Shapley, influence, leverage) reduce to simple class balance. It shows that a fixed top‑k selection rule fails when a point’s value depends on what else has been chosen, leading to excess risk in equal latent‑mode models. The authors propose using class balance when labels reveal learner modes and explicit coverage otherwise, demonstrating that class‑balancing per‑point scores can match or outperform generic coverage on standard classification tasks with pretrained features, while label‑free coverage can improve accuracy by up to +0.20.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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