--- name: iclr-topic-selection description: 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 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. ## Weak ICLR signals - Pure application paper with little learning insight. - Incremental benchmark bump without mechanism, analysis, or robust evidence. - Closed system claim that reviewers cannot inspect or reproduce. - Dataset-only paper without a learning-representation or evaluation advance. - Theory result disconnected from modern learning practice and not routed to a theory-focused venue. ## Routing logic - Prefer NeurIPS or ICML for broader ML method/theory work with less ICLR-specific representation framing. - Prefer AISTATS or UAI for statistics, uncertainty, causal, or probabilistic emphasis. - Prefer ACL, CVPR, KDD, or robotics/HCI venues when the contribution is primarily domain-specific. - Prefer workshops when the idea is timely but under-evidenced. ## Fit-versus-route decision table 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 | ## Worked vignette 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. ## Reviewer-pushback patterns - "No learning insight, just engineering." Reframe around the mechanism or route to a domain venue. - "Dataset-only paper." Add an evaluation or representation advance, or target a datasets-and- benchmarks track instead. - "Theory disconnected from practice." Tie the result to an observed deep-learning phenomenon. ## Output format ```text [ICLR fit] strong / plausible / weak / no [Core learning insight] [Evidence required] [Best venue route] ICLR / NeurIPS / ICML / AISTATS / UAI / domain venue / workshop [Reframe] ```