Structure Learning via Mutual Information

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 MIbased 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. It also contributes new mutual information theoritic foundations to learning algorithms. Figure 1: In the mutual information embedding space, the patterns behind relationship classes are neatly picked out & can be represented in this low-dimensional projection.