--- name: aistats-artifact-evaluation description: Use when packaging AISTATS code, data, proofs, simulation scripts, notebooks, random seeds, and logs as anonymous supplementary evidence or public post-acceptance artifacts, even when there is no separate artifact badge. --- # 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] [Anonymity risks] [Reproduction level] turnkey / scripted / descriptive / weak [Fixes before upload] ```