2D-OOB：通过联合估值框架归因数据贡献
2D-OOB: Attributing Data Contribution Through Joint Valuation Framework
AI总结：
针对现有数据估值方法仅给出单一点级得分、无法区分单元格质量差异的问题，本文提出2D-OOB联合估值袋外估计框架，可同时判定样本贡献及驱动单元格，性能达SOTA且速度更快，适用于细粒度异常值修正、后门触发器定位等场景。
摘要：
数据估值已成为量化每个数据点对机器学习模型训练贡献的强大框架。然而，必须认识到，在实际应用中，单个数据点内的单元格质量可能差异极大。例如，即便某个数据点存在异常，也并非所有单元格都必然含有噪声。现有数据估值方法给出的单一标量得分模糊了数据点中噪声单元格与干净单元格的区别，导致数据值难以解释。本文提出2D-OOB，这是一种袋外估计框架，可联合判定有帮助（或有害）的样本以及驱动这些样本的特定单元格。我们的综合实验表明，2D-OOB在多个用例中达到了最先进的性能，同时速度呈指数级提升。具体而言，2D-OOB在检测和修正单元格级别的细粒度异常值，以及定位数据投毒攻击中的后门触发器方面展现出良好效果。
英文摘要：
Data valuation has emerged as a powerful framework for quantifying each datum's contribution to the training of a machine learning model. However, it is crucial to recognize that the quality of cells within a single data point can vary greatly in practice. For example, even in the case of an abnormal data point, not all cells are necessarily noisy. The single scalar score assigned by existing data valuation methods blurs the distinction between noisy and clean cells of a data point, making it challenging to interpret the data values. In this paper, we propose 2D-OOB, an out-of-bag estimation framework for jointly determining helpful (or detrimental) samples as well as the particular cells that drive them. Our comprehensive experiments demonstrate that 2D-OOB achieves state-of-the-art performance across multiple use cases while being exponentially faster. Specifically, 2D-OOB shows promising results in detecting and rectifying fine-grained outliers at the cell level, and localizing backdoor triggers in data poisoning attacks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
