Learning Theory is a research field devoted to studying the design and analysis of machine learning algorithms. In particular, such algorithms aim at making accurate predictions or representations based on observations.

The emphasis in COLT is on rigorous mathematical analysis using techniques from various connected fields such as probability, statistics, optimization, information theory and geometry. While theoretically rooted, learning theory puts a strong emphasis on efficient computation as well.

The Association for Computational Learning (ACL) is in charge of the organization of the Conference on Learning Theory (COLT), formerly known as the conference on Computational Learning Theory. This conference is held annually since 1988 and has become the leading conference on Learning theory by maintaining a highly selective process for submissions. It is committed in high-quality articles in all theoretical aspects of machine learning and related topics.

Proceedings of COLT automatically accepted in the Conference & Proceedings Track of the Journal of Machine Learning Research if the authors desire to publish a conference paper.