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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.
ICML-Skills/skills/icml-experiments/SKILL.md
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# 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.