基于多臂老虎机进行数据清洗的阈值化 Data Shapley
Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits
AI总结：
针对 Data Shapley 评估数据贡献计算成本高的问题，提出基于阈值化老虎机算法的迭代方法，在保证模型性能的同时快速识别并移除有害实例，有效提升了数据清洗的计算速度。
摘要：
数据清洗旨在通过从训练数据集中移除一组有害实例来提升模型性能。Data Shapley 是一种常见的、具有理论保证的方法，用于评估每个实例对模型性能的贡献；然而，它需要在训练数据的所有子集上进行训练，这在计算上非常昂贵。在本文中，我们提出了一种迭代方法，通过使用阈值化老虎机算法来快速识别具有低 Data Shapley 值的实例子集。我们提供了理论保证，即如果进行足够大数量的迭代，所提出的方法能够准确地选择出有害实例。使用各种模型和数据集进行的实证评估表明，所提出的方法在保持模型性能的同时，有效地提升了计算速度。
英文摘要：
Data cleansing aims to improve model performance by removing a set of harmful instances from the training dataset. Data Shapley is a common theoretically guaranteed method to evaluate the contribution of each instance to model performance; however, it requires training on all subsets of the training data, which is computationally expensive. In this paper, we propose an iterativemethod to fast identify a subset of instances with low data Shapley values by using the thresholding bandit algorithm. We provide a theoretical guarantee that the proposed method can accurately select harmful instances if a sufficiently large number of iterations is conducted. Empirical evaluation using various models and datasets demonstrated that the proposed method efficiently improved the computational speed while maintaining the model performance.
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。
