Skip to main content

Computational Learning Theory

Computational learning theory is an investigation of theoretical aspects of machine learning, of what can and cannot be learned from data. In particular we are interested in the computational efficiency and limitations of learning from large (and small) amounts of data as well as in understanding the theoretical underpinnings of using unlabeled data. Computational learning theory is a multidisciplinary area bringing together techniques and approaches of computer science, statistics and applied mathematics.