--- 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, and PMLR archival status. --- # 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. ## Output format ```text [Eligibility] clear / needs declaration / risky [Closest literatures] [Nearest 3 works] distinction> [Archival-overlap risk] [Novelty sentence] ```