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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-topic-selection --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 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, causality, decision making under uncertainty, or Bayesian reasoning. - Route to COLT if the contribution is mainly formal learning theory and the empirical story is secondary. - Route to a statistics journal when the work needs journal-length exposition, extensive proofs, or a statistics audience more than an AI conference audience. - Check early whether the result can be made convincing in an 8-page submission body. ## Fit signal table | Signal in the project | AI
- Fit test
- Fit signal table
- Vignette: where a debiased estimator goes
- Sharpening moves before committing
- Output format
What does the aistats-topic-selection skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-topic-selection --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.