aistats-artifact-evaluation — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited aistats-artifact-evaluation (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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.
benchmark code, datasets, preprocessing, hyperparameter sweeps, random seeds, logs, or qualitative examples.
in supplementary material.
names, cluster paths, grants, and commit authors.
outputs, runtime, seeds, and known nondeterminism.
reproduction without violating data-use terms.
feasible.
| Claim type | First artifact inspected | Common failure caught |
|---|---|---|
| Convergence rate or regret bound | Proof appendix and constants | Condition used in the proof but missing from the theorem statement |
| Monte Carlo simulation | Seeded simulation script | Plots cannot be regenerated because seeds and replication counts are absent |
| Benchmark comparison | Training and evaluation configs | Baseline tuning budget undocumented |
| Bayesian or MCMC method | Sampler diagnostics and chain logs | No convergence statistics or trace evidence anywhere |
Because AISTATS reviewers are often statisticians, they will rerun a small simulation far more readily than they will retrain a deep model, so make synthetic studies turnkey before polishing anything else.
A hypothetical submission proposes a doubly robust treatment-effect estimator with a root-n normality guarantee, validated on synthetic causal data plus two real benchmarks.
in notebooks, so reviewers can vary n, dimension, and confounding strength.
table; AISTATS-style claims about interval coverage are meaningless without them.
drift apart.
deliberately violates them, since that mapping is what statistical reviewers grade.
one entry script get opened, and design accordingly.
OpenReview submission form rather than past years.
[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>~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.