aistats-topic-selection

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

Vignette: where a debiased estimator goes

A project delivers a debiased lasso variant with valid confidence intervals in high dimensions and simulations confirming coverage. AISTATS reading: strong fit — an inference guarantee plus validating experiments is exactly what this venue rewards. Strip the inference theory and keep only prediction benchmarks, and the same project belongs at a general ML venue; grow it into journal-length asymptotic refinements, and Annals of Statistics or JMLR becomes the better home.