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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-review-process --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 rev
- Process model
- Who reviews here
- Scoring leverage table
- Stage-by-stage realism
- Output format
What does the aistats-review-process skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-review-process --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.