Data Valuation by Leveraging Global and Local Statistical Information
<|reference_start|>Data Valuation by Leveraging Global and Local Statistical Information: 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.<|reference_end|>
@article{zhou2024data,
    title={Data Valuation by Leveraging Global and Local Statistical Information},
    author={Xiaoling Zhou and Ou Wu and Michael K. Ng and Hao Jiang},
    journal={arXiv preprint arXiv:2405.17464},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17464},
    primaryClass={cs.LG cs.AI stat.ML}
}