用广义贝叶斯方法评估数据分布价值，提升标注者与数据增强效果。
Data Distribution Valuation Using Generalized Bayesian Inference
- 基于可迁移性构建损失函数，通过广义贝叶斯推断量化数据分布价值。
- 统一解决标注者评估、数据增强等不同实际问题，实测高效准确。
- 支持连续数据流场景，适用于动态更新的数据评估任务。
我们研究数据分布估值问题，旨在从样本中量化数据分布的价值。这是一个近期提出的新问题，与经典数据估值相关但不同，可应用于多种场景。为此，我们提出一种新框架——广义贝叶斯估值，利用基于可迁移性度量构建的损失函数进行广义贝叶斯推断。该框架能统一解决看似无关的实际问题，如标注者评估和数据增强。借助贝叶斯原理，我们进一步将其扩展至连续数据流设置，增强适用性。实验结果证实了该框架在不同真实场景下的有效性与效率。
原文摘要 · Abstract (English)
We investigate the data distribution valuation problem, which aims to quantify the values of data distributions from their samples. This is a recently proposed problem that is related to but different from classical data valuation and can be applied to various applications. For this problem, we develop a novel framework called Generalized Bayes Valuation that utilizes generalized Bayesian inference with a loss constructed from transferability measures. This framework allows us to solve, in a unified way, seemingly unrelated practical problems, such as annotator evaluation and data augmentation. Using the Bayesian principles, we further improve and enhance the applicability of our framework by extending it to the continuous data stream setting. Our experiment results confirm the effectiveness and efficiency of our framework in different real-world scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。