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kdd-experiments
Use when designing or auditing the empirical section of a KDD paper, where evidence combines quality deltas with scalability and efficiency measurements, temporal-leakage-safe splits, mechanism-isolating ablations, tuning-symmetric baselines, and, for the ADS track, post-launch measurement design that survives the desk check.
KDD-Skills/skills/kdd-experiments/SKILL.md
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# KDD Experiments
Use this while the experimental plan is still cheap to change. A KDD empirical
section answers four questions in order: does the method win, **at what scale**, at
what computational price, and **because of which mechanism**? Papers that answer only
the first question read as ML-flagship rejects retargeted at KDD — a pattern this
venue's reviewers name openly.
## The four-axis evidence plan
| Axis | Minimum credible evidence | Upgrade that wins reviews |
|---|---|---|
| Quality | Headline metric vs tuned baselines on named datasets with stated sizes | Multiple data regimes (sparse/dense, small/large, static/drifting) showing where the method does and does not help |
| Scale | Largest-dataset run with hardware stated | Scaling curve (time and memory vs data size) with the complexity claim overlaid |