From iclr-skills
Evaluates project fit for ICLR vs other ML venues (NeurIPS, ICML, CVPR, ACL, KDD). Helps reframe application papers with representation-learning insights or route to better-matched conferences.
How this skill is triggered — by the user, by Claude, or both
Slash command
/iclr-skills:iclr-topic-selectionThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Use this when a project is still movable. ICLR is broad, but the paper should teach the learning
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.
ICLR's center of gravity is deep representation learning: architectures, self-supervision, generative models, foundation models, RL with deep function approximation, optimization for deep nets, interpretability, and alignment. Score the project against that center before routing.
| Project shape | ICLR fit | Better route if not ICLR |
|---|---|---|
| New self-supervised objective with analysis | Strong | — |
| Theory explaining a deep-net phenomenon | Strong | AISTATS/UAI if purely statistical |
| LLM/foundation-model behavior study | Strong | ACL if narrowly language-specific |
| Benchmark bump, no mechanism | Weak | Domain venue or workshop |
| Causal/uncertainty emphasis | Plausible | AISTATS or UAI |
| Deployed application, little learning insight | Weak | KDD, CVPR, robotics/HCI venue |
A team has a method that improves recommendation click-through in production. As written it is an application paper. To make it ICLR-shaped, they extract the representation-learning claim: a new contrastive objective that yields embeddings transferring across catalogs, demonstrated with an ablation and a probe on a public dataset. The product result becomes one validation point, not the contribution. If that reframing fails to surface a learning insight, the honest route is KDD.
[ICLR fit] strong / plausible / weak / no
[Core learning insight] <one sentence>
[Evidence required] <theory, experiment, benchmark, artifact>
[Best venue route] ICLR / NeurIPS / ICML / AISTATS / UAI / domain venue / workshop
[Reframe] <how to make the paper more ICLR-shaped>
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin iclr-skillsEvaluates whether a manuscript fits ICLR's scope, evidence bar, and submission-cycle requirements. Guides conference-fit diagnosis, re-framing, rebuttal posture, and desk-reject risk assessment for AI/ML flagship venues.
Helps decide whether a manuscript fits ICML, choose between main track and Position Papers, or reroute to another ML venue (NeurIPS, ICLR, etc.).
Guides AI researchers on venue fit for IJCAI/ECAI vs. NeurIPS, ICML, CVPR, ACL, and others. Evaluates main track, special tracks, Survey Track, and AIJ/JAIR expedited-publication routes.