Agent Skills
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aistats-experiments
GitHub用于AISTATS论文实验设计与审计，确保实证结果有效验证理论假设。涵盖基线选择、不确定性报告、泄漏检测及理论与实验的匹配度审查，强调统计严谨性而非排行榜竞赛。
Trigger Scenarios
设计或审计AISTATS论文实验
检查统计测试与不确定性估计
验证理论假设的实验设计
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-experiments -g -y
SKILL.md
Frontmatter
{
"name": "aistats-experiments",
"description": "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 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.
- Add ablations for the mechanism, not just cosmetic variants.
- Audit for leakage, selection bias, multiple-comparison issues, and mismatch between theoretical assumptions and empirical setup.
What experiments are for at this venue
- AISTATS experiments exist to validate theory, not to win leaderboards. One focused simulation confirming a predicted rate outweighs five extra benchmark datasets.
- The strongest design triad: a synthetic study where assumptions hold exactly, a study where they are deliberately violated, and a real-data study showing practical behavior.
- Reviewers, frequently statisticians, check whether the empirical regime — sample size, dimension, noise level — matches the asymptotic regime of the theorems. A bound proven as n grows but tested only at n = 500 invites the question of relevance.
Theory-validation design table
| Theoretical claim | Matching experiment | Reject pattern avoided |
|---|---|---|
| Convergence rate in n | Log-log error versus n with fitted slope | "Rates asserted but never plotted" |
| Confidence-interval coverage | Empirical coverage across many replications | "Nominal 95 percent never verified" |
| Regret bound | Cumulative regret versus horizon, with the bound curve overlaid | "Bound and trajectory never compared" |
| Robustness to misspecification | Violation-severity sweep | "Guarantees hold under assumptions the experiments quietly break" |
Vignette: a kernel conditional independence test
Suppose the paper proves finite-sample type-I error control under a boundedness assumption. The matching plan: simulate under the null at several sample sizes to verify size, sweep dependence strength for power curves, then inject heavy-tailed noise that breaks boundedness to map degradation — every panel tied to a numbered theorem or remark.
Statistical reporting floor
- Replication counts and seeds for every stochastic figure; captions must say whether bars are standard errors, confidence intervals, or quantiles.
- Report the compute actually consumed rather than vague feasibility language.
Output format
[Experiment readiness] strong / adequate / weak
[Claim -> evidence map] <claim: table/figure/simulation>
[Missing statistical evidence] <uncertainty/test/seed/baseline>
[Reproducibility gaps] <hyperparameters/compute/data/code>
[Decision-critical next run] <one experiment or simulation>
Version History
- 1839142 Current 2026-07-05 12:12
