joker-zxr/my-skills/aistats-related-work
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Checking that the SKILL.md file is still in the repository.
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.
Install
npx vskill@latest install joker-zxr/my-skills/aistats-related-workSKILL.md
The same SKILL.md was published earlier as brycewang-stanford/awesome-journal-skills/aistats-related-work.
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
Security
- Security scanPassed, 90 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingplugins/my-skills/resources/Awesome-Journal-Skills/AISTATS-Skills/skills/aistats-related-work/SKILL.md
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