Complete Statistical Theory of Learning (Vladimir Vapnik) | MIT Deep Learning Series

Vladimir Vapnik presents a complete Statistical Learning Theory positing that true intelligence in machine learning arises from incorporating abstract invariants to constrain the admissible function set, rather than relying solely on data-driven brute force. This framework replaces standard empirical risk minimization with a conditional optimization problem where specific predicates, such as symmetry or structural similarities, significantly reduce error rates and mitigate overfitting by shrinking the solution space. Empirical validation on datasets like diabetes and MNIST demonstrates that introducing just a few smart invariants can drastically improve accuracy, challenging current paradigms to achieve high performance with far fewer training samples.