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>
