icml-experiments
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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icml-experiments --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# ICML Experiments Use this before submission or rebuttal when the central issue is whether experiments are sound enough for ICML. The question is not just "does it win"; it is whether the evidence supports the ML claim under fair comparison. ## Experiment audit - Baselines: current, strong, tuned, and correctly implemented. - Ablations: isolate mechanism, architecture, objective, data, or optimization change. - Variance: report seeds, confidence intervals, standard deviations, or a reason variance is not meaningful. - Data: check leakage, split construction, duplication, filtering, licensing, and representative coverage. - Compute: disclose hardware, training cost, inference cost, and comparison fairness. - Scaling: show whether gains persist across model sizes, datasets, horizons, or domains when that supports the claim. - Negative results: use failures to define boundaries rather than hide them. - Appendix: put supporting detail there, but keep decisive evidence in the main 8 pages. ## Reviewer-pushback patterns and the ICML fix | Pushback | Why it lands at ICML | Fix | | --- | --- | --- | | "Convergence guarantees under assumptions the experiments violate" | Theory paper assert
- Experiment audit
- Reviewer-pushback patterns and the ICML fix
- Worked vignette: optimizer claim audit
- Rebuttal-ready result
- Output format
What does the icml-experiments skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icml-experiments --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.