Learning Guarantee

Learning guarantees in machine learning focus on establishing theoretical bounds on the performance of algorithms, ensuring reliable generalization and efficient training. Current research emphasizes deriving such guarantees for diverse settings, including neural networks (analyzing training time and statistical power), adaptive combinatorial optimization (improving approximation guarantees), and online model selection (achieving data-driven regret bounds). These advancements are crucial for building trustworthy and efficient machine learning systems, impacting fields ranging from statistical inference and active learning to domain adaptation and fairness-aware algorithms.