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colt-topic-selection

Use when deciding whether a result is COLT-shaped (Conference on Learning Theory) — a theorem-first learning-theory contribution — versus better routed to ALT, STOC/FOCS, NeurIPS/ICML/AISTATS, JMLR, or a statistics journal, and whether the right vehicle is a full paper or a COLT open-problem piece.

COLT-Skills/skills/colt-topic-selection/SKILL.md

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# COLT Topic Selection

Use this before writing begins. COLT solicits papers on theoretical aspects of machine
learning, described in the 2026 CFP (checked 2026-07-08) as a subject at the
intersection of computer science, statistics, and applied mathematics, with an
explicitly inclusive view that includes theory shedding light on empirical phenomena.
The practical bar: the contribution must be a theorem — a rate, a separation, a
characterization, a hardness result, or an algorithm whose guarantee is the point.

## The three-question fit test

1. **Is the headline sentence a mathematical statement?** "We prove the first
   $O(\sqrt{T})$ regret bound for X" is COLT-shaped. "We propose a method that
   empirically improves X" is not, regardless of how much analysis decorates it.