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>
Source: brycewang-stanford/Awesome-Journal-Skills → AISTATS-Skills/skills/aistats-related-work/SKILL.md
1---2name: aistats-related-work3description: 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.4---567# AISTATS Related Work89Use this to audit novelty and eligibility. Reopen the current CFP for dual-submission,10anonymity, and prior-publication rules before advising authors.1112## Positioning checks1314- Separate statistical novelty from engineering improvement: new estimator, bound,15 inference procedure, optimization analysis, uncertainty method, or empirical insight.16- Compare to both ML conference work and statistics literature; AISTATS reviewers often17 expect both communities to be represented.18- Treat PMLR, journal, and formal conference proceedings as archival unless current rules say19 otherwise.20- Cite arXiv and workshop versions in a way that preserves double-blind review. Do not point21 reviewers to identity-revealing pages.22- Explain overlap with any concurrent or prior version, and do not submit duplicate archival23 work.24- Use related work to sharpen what is new: assumption weakening, finite-sample behavior,25 computational efficiency, uncertainty calibration, robustness, or empirical regime.2627## Two-community coverage table2829| Literature lane | Typical sources | What AISTATS reviewers check |30|---|---|---|31| ML conferences | NeurIPS, ICML, ICLR, UAI, COLT, prior AISTATS volumes in PMLR | Whether the nearest ML method is compared or explicitly distinguished |32| Statistics journals | Annals of Statistics, JMLR, JASA, Biometrika, EJS | Whether classical estimators and known rates are acknowledged |33| Applied statistical fields | Econometrics, biostatistics, epidemiology | Whether identification and inference assumptions follow standard usage |3435A bibliography citing only ML venues tells a statistician reviewer that known statistical36results may be getting rediscovered — a recognizable AISTATS reject pattern that no amount37of benchmark strength repairs.3839## Positioning vignette4041Imagine the paper proposes a variance-reduced off-policy evaluation estimator with an42asymptotic normality result. Its nearest neighbors: a NeurIPS estimator with no inference43guarantee, a JASA semiparametric efficiency bound, and a prior AISTATS paper with a slower44rate. The novelty sentence should name all three contrasts — inference where the ML line had45none, computational tractability where the statistics line stayed abstract, and a sharper46rate than the direct predecessor.4748## Concurrent-work judgment calls4950- Independently concurrent arXiv work: cite neutrally, state the technical difference, and51 avoid priority claims that reviewers cannot verify.52- Your own workshop version: typically non-archival and citable, but verify against the53 current CFP wording and keep the citation phrased so double-blind review survives.54- When in doubt about archival status of a venue, declare the overlap in the submission form55 rather than gambling on a chair's interpretation.5657## Output format5859```text60[Eligibility] clear / needs declaration / risky61[Closest literatures] <ML/statistics/application>62[Nearest 3 works] <work -> distinction>63[Archival-overlap risk] <none/issues>64[Novelty sentence] <AISTATS-ready contribution contrast>65```6667---6869**Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `AISTATS-Skills/skills/aistats-related-work/SKILL.md`
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