Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
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artificial-intelligence-and-statistics
GitHub用于判断论文是否适合AISTATS会议，提供定位、框架调整、证据缺口诊断及拒稿风险评估，适用于统计机器学习领域的投稿策略制定与稿件重构。
触发场景
作者指定AISTATS为目标会场
统计机器学习稿件需进行会议适配性评估
需将期刊或arXiv风格重构为CS会议叙事
需要诊断匿名性、附件或反驳策略
安装
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill artificial-intelligence-and-statistics -g -y
SKILL.md
Frontmatter
{
"name": "artificial-intelligence-and-statistics",
"description": "Use when targeting International Conference on Artificial Intelligence and Statistics (AISTATS) or deciding whether a computer-science manuscript fits this venue. Encodes conference fit, framing, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks for AI statistics."
}
International Conference on Artificial Intelligence and Statistics (AISTATS)
Conference positioning
International Conference on Artificial Intelligence and Statistics (AISTATS) is a top computer-science conference venue for statistical machine learning, probabilistic modeling, causal/statistical inference, and theory-backed methods. It rewards a statistically grounded ML paper with stronger inference, uncertainty, or theory than pure systems novelty. Treat this skill as a fit / venue-selection / re-framing tool for conference submission strategy, not as a substitute for the current year's CFP, author kit, ethics policy, or submission portal.
Because CS conferences change deadlines, templates, page limits, review workflow, artifact rules, AI-use policy, and rebuttal formats every cycle, always verify the live official instructions before making a submission-ready recommendation. Start from the official source anchor recorded for this venue in ../../resources/conference-roster.md and ../../resources/official-source-map.md.
When to trigger
- The author names AISTATS / International Conference on Artificial Intelligence and Statistics as the target venue.
- A manuscript in statistical machine learning needs a conference-fit read before being formatted or submitted.
- The paper must be re-framed from journal style or arXiv style into a selective CS conference narrative.
- The author needs an evidence-gap, anonymity, artifact, rebuttal, or re-routing diagnosis for this venue.
Scope & topic fit
- Core fit: statistical machine learning, probabilistic modeling, causal/statistical inference, and theory-backed methods.
- Best submissions make a precise contribution type visible: algorithm, theorem, system, dataset, benchmark, empirical finding, design artifact, tool, or socio-technical analysis.
- The paper should explain why the result matters to AISTATS's reviewers, not just why it is interesting to the authors' lab or product context.
- Position related work against the most recent conference-cycle papers in this venue and its closest siblings; stale comparisons are a common early-review weakness.
- If the contribution is interdisciplinary, state which part is CS research and which part is domain evidence.
Venue-specific calibration
- Reviewer lens: Read reviewers as statistical ML specialists. Emphasize assumptions, uncertainty, inference, identifiability, and mathematical clarity before application spectacle.
- Contribution hook to foreground: the venue-specific contribution bar.
- Scope vocabulary to use naturally in the abstract and introduction: statistical machine learning, probabilistic modeling, causal/statistical inference, and theory-backed methods.
- Distinctive fingerprint for reviewer calibration: statistical, machine, learning, probabilistic, modeling, causal, inference, theory-backed, methods, venue-specific, contribution, statistics, aistats.
- Official anchor domain: aistats.org. Quote annual rules only after opening that source and the current-year CFP/author kit.
Close-neighbor routing guardrail
- Use this profile only when the manuscript's central contribution is genuinely in AI statistics and the author can say why AISTATS reviewers are the primary audience, not merely a convenient deadline.
- Closest roster neighbors to compare before final routing:
aaai-conference-on-artificial- intelligence(AAAI),international-joint-conference-on-artificial-intelligence(IJCAI),uncertainty-in-artificial-intelligence(UAI),conference-on-learning-theory(COLT). Break ties by contribution type, evidence shape, reviewer community, and the current official CFP from aistats.org.
AISTATS-specific routing detail
- Prefer AISTATS when the contribution is a statistically grounded machine-learning method: inference quality, identifiability, estimation, causal/statistical assumptions, probabilistic modeling, or theory-backed empirical behavior.
- Use UAI when uncertainty representation, graphical models, probabilistic reasoning, or decision-making under uncertainty is the center of the paper rather than one evaluation metric.
- Compare with COLT when theorem depth dominates, ICML/NeurIPS when broad ML methodology or scale is primary, and statistics journals when asymptotic theory or inferential guarantees need fuller exposition.
Method & evidence bar
- Compare against current strong baselines and explain exactly what changes in the algorithm, objective, data, or inference procedure.
- Report ablations that isolate the claimed mechanism; do not rely on aggregate benchmark wins alone.
- Document data, compute, hyperparameters, model selection, and failure cases so the result can be reviewed as science rather than demo output.
- For AISTATS, the evidence must support the venue-specific signature: a statistically grounded ML paper with stronger inference, uncertainty, or theory than pure systems novelty.
- Include limitations, negative results, compute/resource reporting, data provenance, and ethics details when they affect the claim.
Structure & house style
- Frame the contribution as a reusable idea: method, theory, benchmark, dataset, system, or socio-technical finding.
- Separate main claims from exploratory results; reviewers at top AI venues punish overclaiming and hidden cherry-picking.
- Use the current official template exactly; do not guess page limits, font sizes, supplement rules, anonymity exceptions, or camera-ready requirements from old cycles.
- The introduction should answer: problem, why now, what is new, why this venue, and what evidence proves the claim.
- Put the strongest result in the main paper, not only in the appendix or supplement; reviewers should not have to reconstruct the contribution.
Official-cycle checklist
- Open the live official venue page: https://aistats.org/
- Re-check the current cycle's CFP, author kit, submission system, abstract/paper deadlines, page limits, supplementary-material rules, anonymity policy, dual-submission policy, ethics policy, AI-use policy, artifact/code/data expectations, rebuttal/author-response format, and camera-ready requirements.
- Confirm the review workflow and portal: OpenReview / CMT / HotCRP / PCS / START or society portal, as specified for the current cycle.
- Check whether accepted papers require in-person presentation, separate registration, artifact badges, proceedings copyright, or post-acceptance release forms.
- If the live official instructions conflict with this skill, the official instructions win.
Pre-submission self-check
- One sentence states why this manuscript belongs at AISTATS, using the venue's scope rather than generic "top conference" language.
- The claim is calibrated to the evidence: no broader than the datasets, proofs, systems, user studies, deployments, or threat model support.
- Related work includes the nearest current-cycle AI statistics papers and explains the technical delta.
- The paper satisfies the current official template, anonymity, ethics, artifact, and rebuttal requirements.
- The main paper is self-contained enough for reviewers to evaluate novelty and correctness without hunting through external links.
Common desk-reject triggers
- Leaderboard-only novelty with weak explanation of why the method works.
- Unclear data contamination, missing baselines, or evaluation that cannot be reproduced.
- Claims about safety, fairness, health, or society without matching evidence and limitations.
- Formatting, anonymity, dual-submission, external-link, or supplement violations under the current-year policy.
- A contribution framed for a neighboring field while giving AISTATS reviewers too little technical or empirical substance.
Re-routing decision
If the paper misses AISTATS's bar, compare against neural-information-processing-systems / international-conference-on-machine-learning / international-conference-on-learning-representations / aaai-conference-on-artificial-intelligence. Re-route based on contribution type, not prestige: theory to a theory venue, systems to a systems venue, application-heavy work to a domain venue, and early ideas to workshops or shorter tracks when the official CFP supports them.
Output format
[Fit] High / Medium / Low (one-line reason)
[Target] International Conference on Artificial Intelligence and Statistics (AISTATS)
[Contribution type] algorithm / theory / system / dataset / benchmark / empirical / design / security / other
[Main evidence gap] <single most important missing proof, experiment, study, artifact, or policy check>
[Official items to re-check] CFP / author kit / deadline / format / anonymity / ethics / AI-use / artifact / rebuttal / camera-ready
[Top rejection risk] <venue-specific risk>
[Re-route suggestion] <better-matched conference or journal if not a fit>
版本历史
- 1839142 当前 2026-07-05 12:41
