Conference On Learning Theory

Conference on Learning Theory (COLT)

Conference positioning

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.

Scope & topic fit

• Core fit: formal learning theory, statistical learning, online learning, bandits, optimization theory, and lower bounds.
• 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.
• Distinctive fingerprint for reviewer calibration: formal, learning, theory, statistical, online, bandits, optimization, lower, bounds, venue-specific, contribution, learningtheory.
• 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.

Official-cycle checklist

• Open the live official venue page: https://learningtheory.org/colt2026/
• 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>

Source: brycewang-stanford/Awesome-Journal-Skills → Computer-Science-Conference-Skills/skills/conference-on-learning-theory/SKILL.md