Data valuation has garnered increasing attention in recent years, given the
critical role of high-quality data in various applications, particularly in
machine learning tasks. There are diverse technical avenues to quantify the
value of data within a corpus. While Shapley value-based methods are among the
most widely used techniques in the literature due to their solid theoretical
foundation, the accurate calculation of Shapley values is often intractable,
leading to the proposal of numerous approximated calculation methods. Despite
significant progress, nearly all existing methods overlook the utilization of
distribution information of values within a data corpus. In this paper, we
demonstrate that both global and local statistical information of value
distributions hold significant potential for data valuation within the context
of machine learning. Firstly, we explore the characteristics of both global and
local value distributions across several simulated and real data corpora.
Useful observations and clues are obtained. Secondly, we propose a new data
valuation method that estimates Shapley values by incorporating the explored
distribution characteristics into an existing method, AME. Thirdly, we present
a new path to address the dynamic data valuation problem by formulating an
optimization problem that integrates information of both global and local value
distributions. Extensive experiments are conducted on Shapley value estimation,
value-based data removal/adding, mislabeled data detection, and
incremental/decremental data valuation. The results showcase the effectiveness
and efficiency of our proposed methodologies, affirming the significant
potential of global and local value distributions in data valuation.

数据评估在近年来引起了越来越多的关注，鉴于高质量数据在各种应用中的重要作用，特别是在机器学习任务中。本文探讨了全局和局部价值分布的特征，提出了一种基于所探测到的分布特征的新的数据评估方法，并提出了一种解决动态数据评估问题的新路径，通过制定一个整合全局和局部价值分布信息的优化问题。广泛的实验表明了我们提出方法的有效性和高效性，验证了全局和局部价值分布在数据评估中的重要潜力。

利用全球和本地统计信息进行数据估值

Data Valuation by Leveraging Global and Local Statistical Information

This paper settles the sample complexity of single-parameter revenue
maximization by showing matching upper and lower bounds, up to a
poly-logarithmic factor, for all families of value distributions that have been
considered in the literature. The upper bounds are unified under a novel
framework, which builds on the strong revenue monotonicity by Devanur, Huang,
and Psomas (STOC 2016), and an information theoretic argument. This is
fundamentally different from the previous approaches that rely on either
constructing an $\epsilon$-net of the mechanism space, explicitly or implicitly
via statistical learning theory, or learning an approximately accurate version
of the virtual values. To our knowledge, it is the first time information
theoretical arguments are used to show sample complexity upper bounds, instead
of lower bounds. Our lower bounds are also unified under a meta construction of
hard instances.

利用信息理论的方法，通过分析单参数收入最大化问题，对所有已考虑的数值分布族的样本复杂度进行了匹配的上下界的研究。