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.
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 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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.
| Weeks out (heuristic) | Theory-plus-experiments milestone |
|---|---|
| 8+ | Theorems proved, assumption set frozen |
| 6 | Simulations designed against each theorem |
| 4 | Real-data runs complete, seeds logged |
| 3 | Full draft sitting in AISTATS two-column format |
| 2 | Internal mock review by a statistics-minded reader |
| 1 | Checklist pass, anonymity sweep, supplement assembly |
| 0 | Abstract 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.
[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>
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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.
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.
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.
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.
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.
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.