English(EN) Quotient Semivalues for False-Name-Resistant Data Attribution

新方法应对机器学习数据归因中的假名操纵

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-08 12:34

研究人员开发了一种名为商半值机制的新数据归因方法，以应对机器学习中的假名操纵。该方法解决了贡献者可能通过使用不同身份复制或创建合成示例来夸大其数据贡献的问题。该机制在基于证据的数据簇上计算归因值，吸收重复数据，并提供更可靠的贡献度量，尤其是在数据来源不完美的情况下。 AI

影响引入了一种新颖的机制来提高机器学习中数据归因的公平性和准确性，这对于激励诚实的数据贡献至关重要。

排序理由这是一篇详细介绍机器学习中数据归因新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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arXiv cs.LG TIER_1 English(EN) · Brittany I. Davidson · 2026-05-08 12:34

Quotient Semivalues for False-Name-Resistant Data Attribution

Data valuation methods allocate payments and audit training data's contribution to machine-learning pipelines; however, they often assume passive contributors. In reality, contributors can split datasets across pseudonymous identities, duplicate high-value examples, create near-d…