标题： 带有理论保证的迭代数据整理 显示英文标题

标题： Iterative Data Curation with Theoretical Guarantees

摘要： 近年来，越来越多的大数据集变得可用。数据准确性，即数据中没有可验证的错误，在这些大型材料中对于实现高质量的研究、下游应用和模型训练至关重要。这导致了如何在如此庞大且不断增长的数据中进行数据准确性的维护或改进的问题，尤其是在数据太大以至于无法进行人工维护的情况下。本文提出了一种统一的过程，用于数据集的迭代和持续改进。我们提供了理论保证，证明数据准确性测试可以加快错误减少，并且最重要的是，所提出的方法在渐近意义上将以概率一消除数据中的所有错误。我们通过模拟和一个实际案例来验证理论结果。 显示英文摘要

摘要： In recent years, more and more large data sets have become available. Data accuracy, the absence of verifiable errors in data, is crucial for these large materials to enable high-quality research, downstream applications, and model training. This results in the problem of how to curate or improve data accuracy in such large and growing data, especially when the data is too large for manual curation to be feasible. This paper presents a unified procedure for iterative and continuous improvement of data sets. We provide theoretical guarantees that data accuracy tests speed up error reduction and, most importantly, that the proposed approach will, asymptotically, eliminate all errors in data with probability one. We corroborate the theoretical results with simulations and a real-world use case.