CHG Shapley: Efficient Data Valuation and Selection towards Trustworthy Machine Learning

• URL: http://arxiv.org/abs/2406.11730v2
• Date: Tue, 18 Jun 2024 07:38:31 GMT
• Title: CHG Shapley: Efficient Data Valuation and Selection towards Trustworthy Machine Learning
• Authors: Huaiguang Cai,
• Abstract summary: We propose CHG Shapley, which approximates the utility of each data subset on model accuracy during a single model training. We employ CHG Shapley for real-time data selection, demonstrating its effectiveness in identifying high-value and noisy data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Understanding the decision-making process of machine learning models is crucial for ensuring trustworthy machine learning. Data Shapley, a landmark study on data valuation, advances this understanding by assessing the contribution of each datum to model accuracy. However, the resource-intensive and time-consuming nature of multiple model retraining poses challenges for applying Data Shapley to large datasets. To address this, we propose the CHG (Conduct of Hardness and Gradient) score, which approximates the utility of each data subset on model accuracy during a single model training. By deriving the closed-form expression of the Shapley value for each data point under the CHG score utility function, we reduce the computational complexity to the equivalent of a single model retraining, an exponential improvement over existing methods. Additionally, we employ CHG Shapley for real-time data selection, demonstrating its effectiveness in identifying high-value and noisy data. CHG Shapley facilitates trustworthy model training through efficient data valuation, introducing a novel data-centric perspective on trustworthy machine learning.

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