AISTATS Artifact Evaluation
Use this for evidence packaging around AISTATS. The venue centers on artificial intelligence, statistics, and machine learning, so artifacts should make statistical and computational claims inspectable.
Artifact plan
- Decide what evidence reviewers need: proof details, derivations, simulation scripts, benchmark code, datasets, preprocessing, hyperparameter sweeps, random seeds, logs, or qualitative examples.
- Keep decision-critical evidence in the main paper or appendix; optional run files can live in supplementary material.
- Anonymize repository history, paths, notebook metadata, license headers, organization names, cluster paths, grants, and commit authors.
- Include a minimal reproduction map: environment, dependencies, hardware, commands, expected outputs, runtime, seeds, and known nondeterminism.
- For restricted data, give enough provenance and processing detail for credible reproduction without violating data-use terms.
- After acceptance, replace anonymous archives with public, licensed, citable artifacts when feasible.
Output format
text[Artifact role] anonymous supplement / camera-ready release / public archive [Contents] <code/data/proofs/logs/notebooks> [Anonymity risks] <paths/metadata/licenses/URLs> [Reproduction level] turnkey / scripted / descriptive / weak [Fixes before upload] <ordered list>


