The 41 revised full papers presented together with 5 articles on open problems and 2 invited lectures were carefully reviewed and selected from a total of 92 submissions. The papers cover a wide range of topics and are organized in topical sections on unsupervised, semisupervised and active learning, statistical learning theory, inductive inference, regularized learning, kernel methods, SVM, online and reinforcement learning, learning algorithms and limitations on learning, dimensionality reduction, other approaches, and open problems.

This volume contains papers presented at the 20th Annual Conference on Learning Theory (previously known as the Conference on Computational Learning Theory) held in San Diego, USA, June 13-15, 2007, as part of the 2007 Federated Computing Research Conference (FCRC).

The Technical Program contained 41 papers selected from 92 submissions, 5 open problems selected from among 7 contributed, and 2 invited lectures. The invited lectures were given by Dana Ron on “Property Testing: A Learning Theory Perspective,” and by Santosh Vempala on “Spectral Algorithms for Learning and Clustering.” The abstracts of these lectures are included in this volume.