Skip to content
cccskills
FreePublished on GitHub

aistats-review-process

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.

The English summary is being prepared. Showing the original description.

Run score Not tested

See how to install

Files included (1)

  • SKILL.md3.4 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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 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>

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

See all skills by brycewang-stanford

Report a problem with this skill