ICML Skills

Use when auditing an ICML main-track submission for OpenReview, LaTeX formatting, 8-page body, anonymity, supplementary material, impact statement, dual submission, concurrent ICML submissions, reciprocal reviewing, and LLM/prompt-injection policy compliance.

Use when stress-testing ICML experimental evidence before submission or rebuttal, including strong tuned baselines, mechanism-isolating ablations, seed variance and confidence intervals, compute disclosure, data leakage and split construction, reproducibility, negative results, and fit to ICML soundness, originality, and significance scoring.

Use when preparing accepted ICML papers for camera-ready upload, PMLR agreement, public OpenReview record, lay summary, conflict disclosure, registration/presentation choices, format checker, and post-conference revision.

Use when positioning an ICML submission against close ML literature, concurrent ICML submissions, recent public papers, workshop papers, ICLR/AISTATS/NeurIPS neighbors, and prior work under double-blind constraints.

Use when deciding what ICML material belongs in the main 8-page body, same-PDF appendices, supplementary manuscript, code/data supplement, anonymous concurrent-submission PDF, or public camera-ready artifact.

Use when rewriting a machine-learning paper for ICML-style claims, 8-page clarity, soundness/originality/significance framing, impact statement, lay-summary readiness, and reviewer-updateable rebuttal posture.

Use when explaining or diagnosing the ICML review process, including OpenReview, reciprocal reviewing, reviewer/AC behavior, review dimensions, author response, one-round discussion, LLM-review policy, ethics flags, and public review records.

Use when drafting ICML rebuttals and reviewer-author discussion replies under OpenReview double-blind constraints, where authors respond after initial reviews, reviewers may then have one additional discussion round, no revised paper can be uploaded during the period, and responses must stay anonymous. Use to triage objections by soundness, originality, significance, clarity, ethics, and reproducibility for the AC.

Use when strengthening ICML reproducibility evidence, including code/data availability, random seeds, compute disclosure, appendix evidence, impact-statement support, and reviewer-facing reproducibility claims.

Use when deciding whether a manuscript fits ICML, choosing the main research track versus the ICML Position Papers track or another venue (NeurIPS, ICLR, AISTATS, UAI, COLT, MLSys, TMLR, JMLR), or rerouting an ML paper based on its contribution type, strength of evidence, theory-versus-empirical balance, and interest to the broad ICML machine-learning community. Use before committing effort to an ICML submission.

Use when packaging ICML artifacts - code, data, model weights, simulators, benchmarks, proof scripts, notebooks, anonymous repositories, and supplementary code/data ZIPs - for both the double-blind review package and the public release that accompanies accepted PMLR papers. Use when checking anonymity, decision relevance, licensing, and the OpenReview code URL field under current ICML rules.