用互信息构建数据结构，提升模型泛化能力。
Structure Learning via Mutual Information
- 基于互信息设计特征，捕捉数据内在功能关系。
- 在函数分类、回归和跨数据集迁移任务中表现更优。
- 适合对信息理论与元学习感兴趣的科研人员。
本文提出一种基于信息论的机器学习算法设计新方法，聚焦于互信息（MI）。我们构建了一个利用基于互信息的特征来学习和表示数据中函数关系的框架。该方法旨在捕获数据集中的底层信息结构，从而实现更高效且可泛化的学习算法。通过在合成数据和真实数据集上的实验验证，展示了在函数分类、回归及跨数据集迁移等任务中的性能提升。这项工作推动了元学习与自动化机器学习的发展，为如何利用信息论改进算法设计和数据集分析提供了新视角，并提出了新的互信息理论基础以支持学习算法。
原文摘要 · Abstract (English)
This paper presents a novel approach to machine learning algorithm design based on information theory, specifically mutual information (MI). We propose a framework for learning and representing functional relationships in data using MI-based features. Our method aims to capture the underlying structure of information in datasets, enabling more efficient and generalizable learning algorithms. We demonstrate the efficacy of our approach through experiments on synthetic and real-world datasets, showing improved performance in tasks such as function classification, regression, and cross-dataset transfer. This work contributes to the growing field of metalearning and automated machine learning, offering a new perspective on how to leverage information theory for algorithm design and dataset analysis and proposing new mutual information theoretic foundations to learning algorithms.
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