Data Valuation by Fusing Global and Local Statistical Information

TL;DR

This paper enhances data valuation by integrating global and local statistical information into Shapley value estimation, improving accuracy and efficiency, especially in dynamic data scenarios, through novel regularization and inference techniques.

Contribution

It introduces a new method that fuses distribution characteristics into valuation, and a dynamic valuation approach that avoids recomputation, advancing the state-of-the-art in data valuation.

Findings

Improved Shapley value estimation accuracy.

Effective data addition and removal strategies.

Enhanced detection of mislabeled data.

Abstract

Data valuation has garnered increasing attention in recent years, given the critical role of high-quality data in various applications. Among diverse data valuation approaches, Shapley value-based methods are predominant due to their strong theoretical grounding. However, the exact computation of Shapley values is often computationally prohibitive, prompting the development of numerous approximation techniques. Despite notable advancements, existing methods generally neglect the incorporation of value distribution information and fail to account for dynamic data conditions, thereby compromising their performance and application potential. In this paper, we highlight the crucial role of both global and local statistical properties of value distributions in the context of data valuation for machine learning. First, we conduct a comprehensive analysis of these distributions across various simulated and real-world datasets, uncovering valuable insights and key patterns. Second, we propose an enhanced data valuation method that fuses the explored distribution characteristics into two regularization terms to refine Shapley value estimation. The proposed regularizers can be seamlessly incorporated into various existing data valuation methods. Third, we introduce a novel approach for dynamic data valuation that infers updated data values without recomputing Shapley values, thereby significantly improving computational efficiency. Extensive experiments have been conducted across a range of tasks, including Shapley value estimation, value-based data addition and removal, mislabeled data detection, and dynamic data valuation. The results showcase the consistent effectiveness and efficiency of our proposed methodologies, affirming the significant potential of global and local value distributions in data valuation.