본문으로 건너뛰기
cccskills
무료GitHub에 공개

aistats-workflow

Use when planning an AISTATS project timeline from venue fit through abstract submission, full-paper upload, supplementary material, review release, author-reviewer discussion, decision, camera-ready, PMLR publication, registration, presentation, and artifact release, with backward-planning offsets for theory-plus-experiments papers.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(1)

  • SKILL.md3.9 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

AISTATS Workflow

Use this as the project-management skill for an AISTATS submission. Replace all dates with the current official timetable and work backwards from OpenReview cutoffs.

AISTATS is a conference, not a journal: it has no standing editor-in-chief and no article-processing charge. The rotating leadership is the per-edition General Chairs and Program Chairs (2026 General Chairs: Emtiyaz Khan and Yingzhen Li; Program Chairs: Arno Solin and Aaditya Ramdas, verified 2026-06-22), and the cost model is registration fees, not APCs — PMLR proceedings are open-access with no author fee. Conference organizers rotate yearly, so re-check the current CFP and organization page rather than carrying a name forward.

Milestones

  • Venue fit: confirm the contribution is genuinely at the AI, ML, and statistics interface.
  • Evidence lock: freeze theorem statements, assumptions, experiments, simulations, baselines, reproducibility checklist, and supplement contents.
  • Abstract deadline: submit real title, abstract, authors, subject areas, and conflicts.
  • Full-paper deadline: upload anonymous PDF and required OpenReview fields.
  • Supplement deadline: upload anonymized appendix/code/data if current cycle allows or requires separate supplementary material.
  • Review release: triage correctness, statistical validity, novelty, clarity, and reproducibility concerns.
  • Discussion period: draft concise anonymous text-only clarifications; avoid forbidden links or unsupported new results.
  • Decision: archive reviews, discussion, meta-review, decision, and submitted versions.
  • Acceptance: prepare PMLR camera-ready files, registration, in-person presentation, and public artifact release.

Backward plan from the paper deadline

Weeks out (heuristic)Theory-plus-experiments milestone
8+Theorems proved, assumption set frozen
6Simulations designed against each theorem
4Real-data runs complete, seeds logged
3Full draft sitting in AISTATS two-column format
2Internal mock review by a statistics-minded reader
1Checklist pass, anonymity sweep, supplement assembly
0Abstract then full paper on OpenReview; supplement by its own cutoff

These offsets are planning heuristics only — anchor every one of them to the current official timetable, never to a previous cycle's calendar.

Failure modes by stage

  • Theory still moving at week 3 forces last-minute theorem edits nobody has verified — the classic AISTATS correctness reject in the making.
  • Leaving format conversion to the final week surfaces overflow late, because two-column math is far tighter than a one-column working draft.
  • Skipping the mock review forfeits the only chance to hear "your experiments violate Assumption 2" from a friend instead of a reviewer.
  • Building the supplement after the paper deadline under time pressure is how anonymity leaks ship.

Coordination notes

  • Assign one named owner for the reproducibility checklist and another for the anonymity sweep; shared ownership is how both slip.
  • Archive the exact submitted PDF, supplement, and checklist, since discussion-phase replies must quote them precisely.

Output format

[Current stage] idea / experiments / writing / abstract / submission / supplement / discussion / accepted
[Next official deadline] <date and source, or unknown>
[Critical path] <three tasks that determine readiness>
[Risk register] <page/anonymity/statistical evidence/supplement/reproducibility/presentation>
[Owner map] <task -> person or role>

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

brycewang-stanford의 스킬 모두 보기

이 스킬의 문제 신고