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aistats-experiments
Use when designing or auditing AISTATS experiments, simulations, baselines, statistical tests, uncertainty estimates, ablations, random seeds, hyperparameters, compute, dataset handling, and claim-to-evidence fit, with emphasis on experiments that validate theorems rather than chase leaderboards.
AISTATS-Skills/skills/aistats-experiments/SKILL.md
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# AISTATS Experiments
Use this before submission when the empirical or simulation story is not yet locked.
## Experiment audit
- Map each empirical claim to a table, figure, simulation, ablation, or robustness check.
- Include baselines that represent both ML practice and relevant statistical methods.
- Separate synthetic simulations that validate assumptions from real-data experiments that
show practical relevance.
- Report uncertainty for stochastic results: repeated runs, standard errors, confidence
intervals, paired tests, or bootstrap intervals when appropriate.
- Report dataset splits, preprocessing, metrics, hyperparameter search ranges, final chosen
settings, selection criteria, random seeds, hardware, software versions, and runtime.