Statistical Complexity

Statistical complexity quantifies the difficulty of learning or approximating a function from data, focusing on the relationship between sample size, model complexity, and generalization performance. Current research investigates this across diverse models, including neural networks (especially deep ReLU networks) and linear models, employing techniques like Empirical Risk Minimization and gradient descent algorithms to analyze learning efficiency and bounds on generalization error. Understanding statistical complexity is crucial for designing efficient algorithms and interpreting the performance of machine learning models, impacting fields ranging from theoretical computer science to practical applications in natural language processing and sequential decision-making.