Metric Entropy

Metric entropy quantifies the complexity of function spaces, crucial for understanding the sample complexity and generalization ability of machine learning models. Current research focuses on refining metric entropy calculations for specific function classes relevant to neural networks, particularly recurrent networks and over-parameterized two-layer networks, often employing techniques like convex hull analysis. These investigations are vital for establishing theoretical guarantees on learning performance and informing the design of efficient algorithms, impacting areas like dynamical system learning and high-dimensional statistics. Improved understanding of metric entropy contributes to sharper generalization bounds and more accurate assessments of algorithm performance.