PAC Learnability

PAC (Probably Approximately Correct) learnability is a foundational concept in machine learning that investigates whether a given class of functions can be learned efficiently from a finite number of examples. Current research focuses on extending PAC learnability to more complex settings, including those involving neural networks with sparse activations, dynamical systems, and federated learning with incomplete data, as well as exploring the connections between PAC learnability and other learning paradigms like online and reinforcement learning. Understanding PAC learnability provides crucial theoretical foundations for designing efficient and reliable machine learning algorithms and for analyzing their performance guarantees, impacting both theoretical computer science and practical applications.