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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-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.
# 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
- Experiment audit
- What experiments are for at this venue
- Theory-validation design table
- Vignette: a kernel conditional independence test
- Statistical reporting floor
- Output format
What does the aistats-experiments skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-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.