Testable Learning

Testable learning aims to develop robust machine learning algorithms that perform well even when the underlying data distribution deviates from idealized assumptions, replacing unverifiable distributional assumptions with efficiently testable conditions. Current research focuses on developing efficient tester-learner algorithms for concept classes like halfspaces and polynomial threshold functions, often employing moment-matching techniques and addressing challenges posed by adversarial label noise and distribution shifts. This framework offers a more practical and reliable approach to learning by providing provable guarantees on algorithm performance under verifiable conditions, potentially leading to more trustworthy and widely applicable machine learning models.