A comprehensive guide helping authors position their research for the Conference on Learning Theory (COLT). It covers core topics like formal learning theory, statistical learning, optimization, and lower bounds. Use this tool to evaluate venue fit, structure the manuscript as 'theorem-first,' and adhere to specific academic conference submission standards.
Conference on Learning Theory (COLT) is a top computer-science conference venue for formal learning theory, statistical learning, online learning, bandits, optimization theory, and lower bounds. It rewards a theorem-first paper whose contribution is a sharp result, proof technique, or formal separation. Treat this skill as a fit / venue-selection / re-framing tool for conference submission strategy, not as a substitute for the current year's CFP, author kit, ethics policy, or submission portal.
Because CS conferences change deadlines, templates, page limits, review workflow, artifact rules, AI-use policy, and rebuttal formats every cycle, always verify the live official instructions before making a submission-ready recommendation. Start from the official source anchor recorded for this venue in ../../resources/conference-roster.md and ../../resources/official-source-map.md.
When to trigger
The author names COLT / Conference on Learning Theory as the target venue.
A manuscript in formal learning theory needs a conference-fit read before being formatted or submitted.
The paper must be re-framed from journal style or arXiv style into a selective CS conference narrative.
The author needs an evidence-gap, anonymity, artifact, rebuttal, or re-routing diagnosis for this venue.
Best submissions make a precise contribution type visible: algorithm, theorem, system, dataset, benchmark, empirical finding, design artifact, tool, or socio-technical analysis.
The paper should explain why the result matters to COLT's reviewers, not just why it is interesting to the authors' lab or product context.
Position related work against the most recent conference-cycle papers in this venue and its closest siblings; stale comparisons are a common early-review weakness.
If the contribution is interdisciplinary, state which part is CS research and which part is domain evidence.
Venue-specific calibration
Reviewer lens: Read reviewers as theorem-first. Definitions, proof novelty, rates, lower bounds, or separations must carry the paper.
Contribution hook to foreground: the venue-specific contribution bar.
Scope vocabulary to use naturally in the abstract and introduction: formal learning theory, statistical learning, online learning, bandits, optimization theory, and lower bounds.
Official anchor domain: learningtheory.org. Quote annual rules only after opening that source and the current-year CFP/author kit.
Close-neighbor routing guardrail
Use this profile only when the manuscript's central contribution is genuinely in learning
theory and the author can say why COLT reviewers are the primary audience, not merely a
convenient deadline.
Closest roster neighbors to compare before final routing: artificial-intelligence-and- statistics (AISTATS), uncertainty-in-artificial-intelligence (UAI), conference-on- machine-learning-and-systems (MLSys), conference-on-lifelong-learning-agents (CoLLAs).
Break ties by contribution type, evidence shape, reviewer community, and the current
official CFP from learningtheory.org.
Method & evidence bar
For systems/data papers, use realistic workloads, data sizes, and baselines; for theory papers, give exact statements and complete proofs.
Explain the data model, assumptions, complexity, and implementation details at the level reviewers can audit.
Connect the result to a durable database, algorithmic, or theoretical problem rather than a one-off benchmark.
For COLT, the evidence must support the venue-specific signature: a theorem-first paper whose contribution is a sharp result, proof technique, or formal separation.
Include limitations, negative results, compute/resource reporting, data provenance, and ethics details when they affect the claim.
Structure & house style
Lead with the formal or systems problem and the new capability the paper creates.
Use figures or examples to make the model clear before dense proof or system detail.
Use the current official template exactly; do not guess page limits, font sizes, supplement rules, anonymity exceptions, or camera-ready requirements from old cycles.
The introduction should answer: problem, why now, what is new, why this venue, and what evidence proves the claim.
Put the strongest result in the main paper, not only in the appendix or supplement; reviewers should not have to reconstruct the contribution.
Re-check the current cycle's CFP, author kit, submission system, abstract/paper deadlines, page limits, supplementary-material rules, anonymity policy, dual-submission policy, ethics policy, AI-use policy, artifact/code/data expectations, rebuttal/author-response format, and camera-ready requirements.
Confirm the review workflow and portal: OpenReview / CMT / HotCRP / PCS / START or society portal, as specified for the current cycle.
Check whether accepted papers require in-person presentation, separate registration, artifact badges, proceedings copyright, or post-acceptance release forms.
If the live official instructions conflict with this skill, the official instructions win.
Pre-submission self-check
One sentence states why this manuscript belongs at COLT, using the venue's scope rather than generic "top conference" language.
The claim is calibrated to the evidence: no broader than the datasets, proofs, systems, user studies, deployments, or threat model support.
Related work includes the nearest current-cycle learning theory papers and explains the technical delta.
The paper satisfies the current official template, anonymity, ethics, artifact, and rebuttal requirements.
The main paper is self-contained enough for reviewers to evaluate novelty and correctness without hunting through external links.
Common desk-reject triggers
Benchmark gains with no explanation of why the method generalizes.
Theory result whose significance is unclear outside a narrow variant.
Missing implementation details or proof gaps in the central claim.
Formatting, anonymity, dual-submission, external-link, or supplement violations under the current-year policy.
A contribution framed for a neighboring field while giving COLT reviewers too little technical or empirical substance.
Re-routing decision
If the paper misses COLT's bar, compare against acm-sigmod-international-conference-on-management-of-data / international-conference-on-very-large-data-bases / ieee-international-conference-on-data-engineering / acm-symposium-on-theory-of-computing. Re-route based on contribution type, not prestige: theory to a theory venue, systems to a systems venue, application-heavy work to a domain venue, and early ideas to workshops or shorter tracks when the official CFP supports them.
Output format
[Fit] High / Medium / Low (one-line reason)
[Target] Conference on Learning Theory (COLT)
[Contribution type] algorithm / theory / system / dataset / benchmark / empirical / design / security / other
[Main evidence gap] <single most important missing proof, experiment, study, artifact, or policy check>
[Official items to re-check] CFP / author kit / deadline / format / anonymity / ethics / AI-use / artifact / rebuttal / camera-ready
[Top rejection risk] <venue-specific risk>
[Re-route suggestion] <better-matched conference or journal if not a fit>
