Computable Learning

Computable learning investigates the intersection of machine learning and computability theory, aiming to understand which learning problems can be solved by algorithms with feasible computational constraints. Current research focuses on characterizing learnable hypothesis classes, particularly within the context of Probably Approximately Correct (PAC) learning, and developing efficient algorithms for specific tasks like learning linear threshold functions from label proportions or incrementally updating neural network predictions for dynamic inputs. This field bridges theoretical computer science and machine learning, providing crucial insights into the fundamental limits of efficient learning and informing the design of practically feasible learning systems.