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

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.