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iclr-topic-selection
Use when deciding whether a project is a strong ICLR submission, should be reframed for ICLR, or should be routed to NeurIPS, ICML, AAAI, AISTATS, ACL, CVPR, KDD, or another venue. Use when a project lacks a clear representation-learning insight, when an application result needs a learning contribution to fit ICLR, or when weighing ICLR's deep-learning center of gravity against a better-matched venue.
ICLR-Skills/skills/iclr-topic-selection/SKILL.md
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# ICLR Topic Selection
Use this when a project is still movable. ICLR is broad, but the paper should teach the learning
community something about representations, objectives, models, data, optimization, evaluation, or
deployment.
## Strong ICLR signals
- A clear representation-learning, model-behavior, optimization, generative modeling, RL, theory, or
evaluation contribution.
- Evidence that changes how researchers should build, analyze, or judge learning systems.
- A simple central claim that can be verified by focused theory, experiments, or artifacts.
- Interest beyond one dataset, product, or application vertical.
- Honest limitations and ethics treatment for high-impact model or data claims.