Informed Machine Learning: Excess risk and generalization

Abstract

Machine Learning (ML) has transformed both research and industry by offering powerful models capable of capturing complex phenomena. However, these models often require large, high-quality datasets and may struggle to generalize beyond the distributions on which they are trained. Informed Machine Learning (IML) tackles these challenges by incorporating domain knowledge at various stages of the ML pipeline, thereby reducing data requirements and enhancing generalization. Building on statistical learning theory, we present some theoretical comparison and insights about ML and IML excess risk and generalization performance. We then illustrate how these theoretical insights can be leveraged in practice through some practical examples. Our findings shed some light on the mechanisms and conditions under which IML can outperform traditional ML, offering valuable guidance for effective implementation in real-world settings.