PAC Learning

Probably Approximately Correct (PAC) learning is a foundational framework in machine learning that studies the sample complexity and computational efficiency of learning algorithms. Current research focuses on extending PAC learning to more complex scenarios, including noisy data, multiple data distributions, and adversarial settings, often employing techniques like Empirical Risk Minimization (ERM) and perturbed gradient descent. These advancements aim to improve the robustness and efficiency of learning algorithms, leading to more reliable and practical machine learning models across various applications. Furthermore, recent work explores the connections between PAC learnability and other learning paradigms, such as online and differentially private learning, providing a deeper understanding of the fundamental limits of learning.