AISTATS Review Process

SkillDev tools

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.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the AISTATS Review Process skill

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in AISTATS-Skills/skills/aistats-review-process/SKILL.md and read by ahel’s review.

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 dimensionWhat raises itWhat sinks it
CorrectnessComplete assumption statements with a main-text proof sketchHidden conditions; constants swept into O-notation when they matter
SignificanceA guarantee the ML literature lacked, or a practical method statistics lackedIncremental rate gain with no conceptual or practical payoff
Empirical supportExperiments engineered to probe the theoryBenchmarks disconnected from the theorem regimes
ClarityNumbered assumptions and a single notation sourceNotation 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>

Signals

GitHub stars
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Aug 2026
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skill
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aistats-review-process
Source
github.com/brycewang-stanford/awesome-journal-skills