Call for Papers

The 37th Annual Conference on Learning Theory (COLT 2024) will take place June 30th-July 3rd, 2024 in Edmonton, Canada. 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
  • 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

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.


COLT will award both best paper and best student paper awards. To be eligible for the best student paper award, the primary contributor(s) must be full-time students at the time of submission. The program committee may decline to make these awards, or may split them among several papers.


Conferences: In general, submissions that are substantially similar to papers that have been previously published, accepted for publication, or submitted in parallel to other peer-reviewed conferences with proceedings may not be submitted to COLT. (An exception is made for STOC 2024 submissions which have not yet received a decision. Any such papers must be withdrawn immediately upon being accepted to STOC 2024.)

Journals: In general, submissions that are substantially similar to papers that have been previously published, accepted for publication, or submitted in parallel to journals may not be submitted to COLT.


As in previous years, there will be a rebuttal phase during the review process. Initial reviews will be sent to authors before final decisions have been made. Authors will have an opportunity to address the issues brought up in the reviews.


We strongly encourage constructive feedback that can help authors improve their work. The aim of the reviewing process is to assess whether the work is close to being ready for publication; as such, the interaction between authors and referees is meant to both figure this out and guide the paper into a publishable state.

We recommend the following video for a thoughtful discussion of such aims and related issues: IACR Distinguished Lecture: Caught in Between Theory and Practice


(All dates are in 2024.)

  • Submission deadline: February 9, 4:00 PM EST
  • Author notification: On or before May 10
  • Conference dates: June 30–July 3

Submission Instructions


Formatting: This year, the submissions are limited to 12 PMLR formatted pages, excluding references. An additional supplementary file may be uploaded that can include unlimited appendices. Please note that in a change from previous years, appendices must be uploaded as a separate file, and the main submission file is limited to 12 pages of text (excluding references).

All details, proofs and derivations required to substantiate the results must be included in the submission, possibly in the appendices. However, the contribution, novelty and significance of submissions will be judged primarily based on the main paper (without appendices), and so enough details, including proof details, must be provided in the main text to convince the reviewers of the submissions' merits.

Anonymization: Submissions should be suitable for double-blind reviewing; in particular, submissions should NOT include author names or other identifying information. To the extent possible, you should avoid including directly identifying information in the text. You should still include all relevant references, discussion, and scientific content, even if this might provide significant hints as to the authors’ identity. But you should generally refer to your own prior work in third person. Do not include acknowledgments in the submission. They can be added in the camera-ready version of accepted papers.

While submissions are anonymized, and author names are withheld from reviewers, they are known to the area chair overseeing the paper’s review. The assigned area chair is allowed to reveal author names to a reviewer during the rebuttal period, upon the reviewer's request, if they deem such information is needed in ensuring a proper review.

Style files: Please use the following style files. The "[anon]" option in the LaTeX template should be used to suppress author names from appearing in the submission.


Papers should be submitted through CMT; the deadline for submissions is February 9, 2024, 4pm EST (New York local time).

Please contact the COLT program chairs if you have any questions about the policy or technical issues with the submission process.

Call for Open Problems

The Conference on Learning Theory (COLT) 2024 will feature a session devoted to the presentation of open problems. A description of these problems will also appear in the COLT proceedings. The deadline for submission is Thursday May 30, 2024, 1pm EDT (Eastern Time). The problems should be related to the COLT range of topics and should have a theoretical nature. The write-up of an open problem should include:

  • A clearly defined problem.
  • The motivation for studying the problem, including why it is important to the COLT community.
  • The current state of this problem, including any known partial or conjectured solutions and relevant references.

Open problems can either be problems that have not been previously stated publicly and investigated theoretically, or problems that have already been suggested in published work. In the first case, the submission should provide convincing arguments for the interest in addressing them. In the second case, the submission should include enough new content to merit a renewed discussion.

You should be able (and will be expected) to clearly express the problem in a short presentation during the open problems session. Note that a monetary reward, or non-monetary (but fun!) prize, is a great way to stimulate interest in solving the open problem.

Submissions should be at most 4 pages long, excluding references, and should be in the COLT 2024 format. The title should start with "Open Problem:". Submissions are non-anonymous; that is, they should contain authors' names (do not use the "anon" option). Submissions should be made to the Open Problems track on the COLT'24 CMT submission site.

Examples of open problems from past years (search for "Open Problem"):

Notification of acceptance or rejection will be sent out by Tuesday June 11, 2024.

For questions please contact the COLT 2024 open problems chair, Tim van Erven, at [email protected].