Connect

COLT Artifact Evaluation

brycewang-stanfordbybrycewang-stanford
•
1.1k
•
Learning & Documentation

Prepares and structures proof verification ledgers, external hypothesis audits, and numerical companions for Conference on Learning Theory (COLT) submissions.

COLT Artifact Evaluation guides theoretical machine learning researchers preparing submissions for the Conference on Learning Theory (COLT). Because COLT does not run a traditional code artifact track or award reproducibility badges, the mathematical proof appendix serves as the primary artifact. This skill helps authors establish machine-checkable proof ledgers, audit external theorem citations and hypotheses, identify unverified dependency gaps, and package supplementary numerical scripts or formal proof mechanizations according to theoretical venue norms.

Key Features

01External theorem dependency and hypothesis boundary auditing
021,074 GitHub stars
03Proof ledger tracking for lemmas, dependencies, and coauthor verification sign-offs
04Post-acceptance repository release checklist for arXiv and PMLR camera-ready
05Formal proof mechanization guidance for Lean and Coq companions
06Best practices for packaging minimal numerical illustration scripts and seeds

Use Cases

01Preparing post-acceptance code repositories and formal proof releases
02Structuring lightweight numerical simulations according to theory conference expectations
03Auditing mathematical proofs and lemma dependency chains prior to COLT paper submission