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

[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>