Individual skill
icml-topic-selection
Use when deciding whether a manuscript fits ICML, choosing the main research track versus the ICML Position Papers track or another venue (NeurIPS, ICLR, AISTATS, UAI, COLT, MLSys, TMLR, JMLR), or rerouting an ML paper based on its contribution type, strength of evidence, theory-versus-empirical balance, and interest to the broad ICML machine-learning community. Use before committing effort to an ICML submission.
ICML-Skills/skills/icml-topic-selection/SKILL.md
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# ICML Topic Selection
Use this before committing to ICML. ICML rewards original, rigorous machine-learning research of
significant interest to the ML community. It is not the best route for every AI application or
position argument.
## Strong fit
- A core ML method, theory, optimization, probabilistic model, RL algorithm, evaluation method,
systems contribution, or trustworthy-ML result.
- A use-inspired paper where the ML technique, evaluation, or insight is itself important to the ML
community.
- A theory paper with clear assumptions and meaningful implications.
- An empirical study that improves how ML is evaluated, reproduced, scaled, or understood.