论文标题
有效的计算和分布分布莎普利值
Efficient computation and analysis of distributional Shapley values
论文作者
论文摘要
分销数据Shapley值（DSHAPLEY）最近被提议作为量化单个基准在机器学习中的贡献的原则框架。 DSHAPLEY将Shapley值的基础游戏理论概念开发到一个统计框架中，并可以应用于确定对学习算法有用（或有害）的数据点。但是，估算DSHAPLEY在计算上很昂贵，但是在实践中使用它可能是一个主要的挑战。此外，几乎没有关于该值如何取决于数据特征的数学分析。在本文中，我们得出了DSHAPLEY的第一个分析表达式，用于线性回归，二元分类和非参数密度估计的规范问题。这些分析形式为估计dshapley提供了新的算法，这些算法比以前的最新方法快几个数量级。此外，我们的公式是直接解释的，并提供了有关该价值如何在不同类型数据变化的定量见解。我们证明了方法对多个真实和合成数据集的实际功效。
Distributional data Shapley value (DShapley) has recently been proposed as a principled framework to quantify the contribution of individual datum in machine learning. DShapley develops the foundational game theory concept of Shapley values into a statistical framework and can be applied to identify data points that are useful (or harmful) to a learning algorithm. Estimating DShapley is computationally expensive, however, and this can be a major challenge to using it in practice. Moreover, there has been little mathematical analyses of how this value depends on data characteristics. In this paper, we derive the first analytic expressions for DShapley for the canonical problems of linear regression, binary classification, and non-parametric density estimation. These analytic forms provide new algorithms to estimate DShapley that are several orders of magnitude faster than previous state-of-the-art methods. Furthermore, our formulas are directly interpretable and provide quantitative insights into how the value varies for different types of data. We demonstrate the practical efficacy of our approach on multiple real and synthetic datasets.
