Conference Information

COLT 2026: Annual Conference on Learning Theory

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Submission Date:
2026-02-04
Notification Date:
2026-05-04
Conference Date:
2026-06-29
Location:
San Diego, California, USA
Years:
39
CCF: B   ICORE: A*   QUALIS: A2   Viewed: 367005   Tracked: 114   Attend: 14

Conference Partner Index (CP-I)

89.2 / 100
Ranked #83 of 5,682 conferences · Top 2%

#9 of 739 in Artificial Intelligence & Machine Learning

Academic recognition (35%)
100
Submission selectivity (20%)
79
Editions held (20%)
99
Community attention (10%)
65
Public record completeness (15%)
80

Inputs used: Listed as CCF B, QUALIS A2 · Acceptance rate: 29.3% (mean of 5 editions on file) · Editions on record: 39 · Researchers following it here: 114 · Researchers who opened this page in the past 24 months: 12

Missing from the public record: Past editions (+3.0)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 100% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-18

Call For Papers

COLT 2026 (Annual Conference on Learning Theory) is a CCF B / ICORE A* / QUALIS A2 conference held in San Diego, California, USA on 2026-06-29. The paper submission deadline is 2026-02-04. Acceptance notifications are sent on 2026-05-04.

The 39th Annual Conference on Learning Theory (COLT 2026) will take place June 29-July 3, 2026 in San Diego, USA. We invite submissions of papers addressing theoretical aspects of machine learning, broadly defined as a subject at the intersection of computer science, statistics and applied mathematics. We strongly support an inclusive view of learning theory, including fundamental theoretical aspects of learnability in various contexts, and theory that sheds light on empirical phenomena. The topics include but are not limited to: Design and analysis of learning algorithms Statistical and computational complexity of learning Optimization methods for learning, including online and stochastic optimization Theory of artificial neural networks, including deep learning Theoretical explanation of empirical phenomena in learning Supervised learning Unsupervised, semi-supervised learning, domain adaptation Learning geometric and topological structures in data, manifold learning Active and interactive learning Reinforcement learning Online learning and decision-making Interactions of learning theory with other mathematical fields High-dimensional and non-parametric statistics Kernel methods Causality Sampling Theoretical analysis of probabilistic graphical models Bayesian methods in learning Game theory and learning Learning with system constraints (e.g., privacy, fairness, memory, communication) Learning from complex data (e.g., networks, time series) Learning in neuroscience, social science, economics and other subjects Quantum learning theory Submissions by authors who are new to COLT are encouraged. While the primary focus of the conference is theoretical, authors are welcome to support their analysis with relevant experimental results. Accepted papers will be presented at the conference. At least one author of each accepted paper should present the work at the conference. Accepted papers will be published electronically in the Proceedings of Machine Learning Research (PMLR). Authors of accepted papers will have the option of opting out of the proceedings in favor of a 1-page extended abstract, which will point to an open access archival version of the full paper reviewed for COLT.
Last updated by Dou Sun on

Acceptance Ratio

Average acceptance rate: 36.7% over 17 years (2000–2020).

YearSubmittedAcceptedAccepted(%)
202038812030.9%
201939311830%
20183359127.2%
20172287432.5%
20162035326.1%
20151786234.8%
20141405237.1%
20131314735.9%
20121264132.5%
20081264434.9%
2007924144.6%
20061024342.2%
20051204537.5%
20041074441.1%
2003924953.3%
2002552647.3%
20001726236%

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