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aistats-topic-selection
Use when deciding whether a project is a strong AISTATS fit, comparing AISTATS with NeurIPS, ICML, ICLR, UAI, COLT, JMLR, statistics journals, or application venues, identifying the statistical primitive of the contribution, and sharpening the AI-statistics framing before writing begins.
AISTATS-Skills/skills/aistats-topic-selection/SKILL.md
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# AISTATS Topic Selection
Use this before writing. AISTATS is strongest for work at the intersection of artificial
intelligence, machine learning, and statistics, especially when statistical reasoning is not
merely an evaluation detail.
## Fit test
- Prefer AISTATS when the contribution advances statistical foundations, inference,
uncertainty, causal or probabilistic modeling, learning theory, optimization, or empirical
methodology with clear AI/ML relevance.
- Route to ICML, NeurIPS, or ICLR if the main contribution is broad ML systems, representation
learning, scaling, or deep learning practice with limited statistical novelty.
- Route to UAI if the contribution is primarily uncertainty, probabilistic graphical models,