aistats-related-work
GitHub用于审计AISTATS投稿的创新性与合规性。区分统计与工程贡献，覆盖ML和统计学文献以符合双社区期望，处理arXiv及会议版本的匿名引用，评估并发工作与档案重叠风险，确保满足双重盲审要求。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-related-work -g -y
SKILL.md
Frontmatter
{
"name": "aistats-related-work",
"description": "Use when positioning an AISTATS submission against AI, machine-learning, statistics, and uncertainty literature, including arXiv preprints, workshop versions, concurrent submissions, prior conference versions, PMLR archival status, and the two-community citation coverage that AISTATS reviewers expect."
}
AISTATS Related Work
Use this to audit novelty and eligibility. Reopen the current CFP for dual-submission, anonymity, and prior-publication rules before advising authors.
Positioning checks
- Separate statistical novelty from engineering improvement: new estimator, bound, inference procedure, optimization analysis, uncertainty method, or empirical insight.
- Compare to both ML conference work and statistics literature; AISTATS reviewers often expect both communities to be represented.
- Treat PMLR, journal, and formal conference proceedings as archival unless current rules say otherwise.
- Cite arXiv and workshop versions in a way that preserves double-blind review. Do not point reviewers to identity-revealing pages.
- Explain overlap with any concurrent or prior version, and do not submit duplicate archival work.
- Use related work to sharpen what is new: assumption weakening, finite-sample behavior, computational efficiency, uncertainty calibration, robustness, or empirical regime.
Two-community coverage table
| Literature lane | Typical sources | What AISTATS reviewers check |
|---|---|---|
| ML conferences | NeurIPS, ICML, ICLR, UAI, COLT, prior AISTATS volumes in PMLR | Whether the nearest ML method is compared or explicitly distinguished |
| Statistics journals | Annals of Statistics, JMLR, JASA, Biometrika, EJS | Whether classical estimators and known rates are acknowledged |
| Applied statistical fields | Econometrics, biostatistics, epidemiology | Whether identification and inference assumptions follow standard usage |
A bibliography citing only ML venues tells a statistician reviewer that known statistical results may be getting rediscovered — a recognizable AISTATS reject pattern that no amount of benchmark strength repairs.
Positioning vignette
Imagine the paper proposes a variance-reduced off-policy evaluation estimator with an asymptotic normality result. Its nearest neighbors: a NeurIPS estimator with no inference guarantee, a JASA semiparametric efficiency bound, and a prior AISTATS paper with a slower rate. The novelty sentence should name all three contrasts — inference where the ML line had none, computational tractability where the statistics line stayed abstract, and a sharper rate than the direct predecessor.
Concurrent-work judgment calls
- Independently concurrent arXiv work: cite neutrally, state the technical difference, and avoid priority claims that reviewers cannot verify.
- Your own workshop version: typically non-archival and citable, but verify against the current CFP wording and keep the citation phrased so double-blind review survives.
- When in doubt about archival status of a venue, declare the overlap in the submission form rather than gambling on a chair's interpretation.
Output format
[Eligibility] clear / needs declaration / risky
[Closest literatures] <ML/statistics/application>
[Nearest 3 works] <work -> distinction>
[Archival-overlap risk] <none/issues>
[Novelty sentence] <AISTATS-ready contribution contrast>
Version History
- 1839142 Current 2026-07-05 12:12
