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name: aistats-review-process description: Use when explaining or planning around AISTATS peer review, OpenReview review release, author-reviewer discussion, reviewer volunteer expectations, reviewer confidentiality, decision criteria, meta-review dynamics, the statistician-heavy reviewer pool, and PMLR proceedings outcomes.

AISTATS Review Process

Use this to reason about review-stage strategy. Reopen the current CFP, OpenReview group, author instructions, reviewer instructions if posted, and code of conduct before making process claims.

Process model

• AISTATS uses OpenReview for submission and review workflow in recent cycles.
• Reviewers evaluate technical correctness, statistical and machine-learning contribution, empirical support, clarity, reproducibility, and relevance to artificial intelligence and statistics.
• Author discussion is limited. AISTATS 2026 used a discussion period after initial reviews, with text-only author-reviewer discussion and no links.
• Reviewer and author obligations include confidentiality, appropriate conflicts, professional conduct, and respect for anonymity.
• The most useful response is a decision-focused clarification that gives the area chair or meta-reviewer a clean rationale for acceptance or rejection.
• Accepted papers are published in PMLR, so final metadata and camera-ready compliance matter as much as the initial acceptance.

Who reviews here

• The pool mixes ML researchers with statisticians and statistical learning theorists; expect at least one reviewer to read proofs and assumption sets line by line.
• Because AISTATS is smaller and more specialized than NeurIPS or ICML, topical matches are closer, so vague proof sketches get caught rather than skimmed past.
• Borderline theory-plus-experiments papers usually fall on one of three edges: an assumption the experiments do not satisfy, a missing classical-statistics baseline, or a rate claim never checked empirically.

Scoring leverage table

Review dimension	What raises it	What sinks it
Correctness	Complete assumption statements with a main-text proof sketch	Hidden conditions; constants swept into O-notation when they matter
Significance	A guarantee the ML literature lacked, or a practical method statistics lacked	Incremental rate gain with no conceptual or practical payoff
Empirical support	Experiments engineered to probe the theory	Benchmarks disconnected from the theorem regimes
Clarity	Numbered assumptions and a single notation source	Notation collisions between sections

Stage-by-stage realism

• Initial reviews: triage by what the meta-reviewer would weigh, not by reviewer tone.
• Discussion: windows are short; an early, precise reply is worth more than a late comprehensive one.
• Decision: the meta-review synthesizes; one unanswered correctness objection outweighs several resolved clarity complaints.
• Reviewer-volunteer expectations for submitting authors have appeared in recent cycles; confirm the current CFP rather than assuming either way.

Output format

[Current stage] submitted / reviews / discussion / decision / camera-ready
[Decision actors] <reviewers/meta-reviewer/chairs>
[Likely leverage] <correctness/statistics/experiments/clarity/reproducibility>
[Forbidden moves] <identity leak / external links if forbidden / new unsupported results>
[Next response move] <one action>