Call for Papers

(Submission instructions are below.)

The 34rd Annual Conference on Learning Theory (COLT 2021) will take place July 7-10, 2021. Assuming the circumstances allow for an in-person conference it will be held at CU Boulder, in Colorado. 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 in both oral and poster sessions. 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.

PAPER AWARDS

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. For eligible papers, authors must indicate at submission time if they wish their paper to be considered for a student paper award. The program committee may decline to make these awards, or may split them among several papers.

DUAL SUBMISSIONS POLICY

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. The only exception is for papers under submission to STOC 2021, as detailed below.

Dual submission with STOC 2021: The STOC 2021 notification date falls on February 7. We will allow submissions that are substantially similar to papers that have been submitted to STOC 2021, provided that the authors (1) declare such dual submissions through the submission server, and (2) immediately withdraw the COLT submission if the STOC submission is accepted.

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.

REBUTTAL PHASE

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.

IMPORTANT DATES

(All dates are in 2021.)

  • Paper submission deadline: January 29, 4:00 PM PST
  • Author feedback opens: March 31
  • Author notification: May 14
  • Conference: July 7-10

Submisison Instructions

FORMATTING AND ANONYMIZATION

Formatting: Submissions are limited to 12 PMLR-formatted pages, plus unlimited additional pages for references and appendices. 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 text (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 author 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.

SUBMITTING YOUR PAPER

Papers should be submitted through CMT; the deadline for submissions is January 29, 2021 at 4:00 PM PST.

Please contact the COLT program chairs at [email protected] if you have any questions about the policy or technical issues with the submission process. We looking forward to reading your submissions!