The summary Claude sees in its skill listing — used to decide when to auto-load this skill
International Conference on Learning Representations (ICLR) is a top computer-science conference venue for representation learning, deep learning, generative models, optimization, and open-review discussion. It rewards a learning-representation paper that can survive public review, revision, and comparison to fast-moving prior work. Treat this skill as a **fit / venue-selection / re-framing** t...
International Conference on Learning Representations (ICLR)
Conference positioning
International Conference on Learning Representations (ICLR) is a top computer-science conference venue for representation learning, deep learning, generative models, optimization, and open-review discussion. It rewards a learning-representation paper that can survive public review, revision, and comparison to fast-moving prior work. Treat this skill as a fit / venue-selection / re-framing tool for conference submission strategy, not as a substitute for the current year's CFP, author kit, ethics policy, or submission portal.
Because CS conferences change deadlines, templates, page limits, review workflow, artifact rules, AI-use policy, and rebuttal formats every cycle, always verify the live official instructions before making a submission-ready recommendation. Start from the official source anchor recorded for this venue in ../../resources/conference-roster.md and ../../resources/official-source-map.md.
When to trigger
The author names ICLR / International Conference on Learning Representations as the target venue.
A manuscript in representation learning needs a conference-fit read before being formatted or submitted.
The paper must be re-framed from journal style or arXiv style into a selective CS conference narrative.
The author needs an evidence-gap, anonymity, artifact, rebuttal, or re-routing diagnosis for this venue.
Scope & topic fit
Core fit: representation learning, deep learning, generative models, optimization, and open-review discussion.
Best submissions make a precise contribution type visible: algorithm, theorem, system, dataset, benchmark, empirical finding, design artifact, tool, or socio-technical analysis.
The paper should explain why the result matters to ICLR's reviewers, not just why it is interesting to the authors' lab or product context.
Position related work against the most recent conference-cycle papers in this venue and its closest siblings; stale comparisons are a common early-review weakness.
If the contribution is interdisciplinary, state which part is CS research and which part is domain evidence.
Venue-specific calibration
Reviewer lens: Read reviewers as cross-area AI specialists. The paper needs a clear ML or AI research contribution, strong baselines, honest limitations, and enough breadth to matter outside one lab benchmark.
Contribution hook to foreground: the venue-specific contribution bar.
Scope vocabulary to use naturally in the abstract and introduction: representation learning, deep learning, generative models, optimization, and open-review discussion.
Official anchor domain: iclr.cc. Quote annual rules only after opening that source and the current-year CFP/author kit.
Close-neighbor routing guardrail
Route to ICLR when the paper is about representation learning, deep-learning methods,
architectures, optimization behavior, or empirically grounded learning insights suited to
open review.
Compare ICML for broader ML methods/theory, NeurIPS for broad ML/AI impact, AISTATS/UAI/COLT
for statistics/uncertainty/theory, and domain venues for application-first work.
What distinguishes this venue from its closest siblings
What ICLR is. The International Conference on Learning Representations — deep/representation learning, OpenReview open-review model.
vs ICML / NeurIPS. ICML (IMLS) and NeurIPS are the other two ML flagships; ICLR is deep-learning-forward with public OpenReview — route by cycle and topic emphasis, not prestige.
Routing. Vision-specific → CVPR/ICCV/ECCV; NLP → ACL-family; theory → COLT.
Method & evidence bar
Compare against current strong baselines and explain exactly what changes in the algorithm, objective, data, or inference procedure.
Report ablations that isolate the claimed mechanism; do not rely on aggregate benchmark wins alone.
Document data, compute, hyperparameters, model selection, and failure cases so the result can be reviewed as science rather than demo output.
For ICLR, the evidence must support the venue-specific signature: a learning-representation paper that can survive public review, revision, and comparison to fast-moving prior work.
Include limitations, negative results, compute/resource reporting, data provenance, and ethics details when they affect the claim.
Structure & house style
Frame the contribution as a reusable idea: method, theory, benchmark, dataset, system, or socio-technical finding.
Separate main claims from exploratory results; reviewers at top AI venues punish overclaiming and hidden cherry-picking.
Use the current official template exactly; do not guess page limits, font sizes, supplement rules, anonymity exceptions, or camera-ready requirements from old cycles.
The introduction should answer: problem, why now, what is new, why this venue, and what evidence proves the claim.
Put the strongest result in the main paper, not only in the appendix or supplement; reviewers should not have to reconstruct the contribution.
Re-check the current cycle's CFP, author kit, submission system, abstract/paper deadlines, page limits, supplementary-material rules, anonymity policy, dual-submission policy, ethics policy, AI-use policy, artifact/code/data expectations, rebuttal/author-response format, and camera-ready requirements.
Confirm the review workflow and portal: OpenReview and the current-year official author guide.
Check whether accepted papers require in-person presentation, separate registration, artifact badges, proceedings copyright, or post-acceptance release forms.
If the live official instructions conflict with this skill, the official instructions win.
Pre-submission self-check
One sentence states why this manuscript belongs at ICLR, using the venue's scope rather than generic "top conference" language.
The claim is calibrated to the evidence: no broader than the datasets, proofs, systems, user studies, deployments, or threat model support.
Related work includes the nearest current-cycle AI/ML flagship papers and explains the technical delta.
The paper satisfies the current official template, anonymity, ethics, artifact, and rebuttal requirements.
The main paper is self-contained enough for reviewers to evaluate novelty and correctness without hunting through external links.
Common desk-reject triggers
Leaderboard-only novelty with weak explanation of why the method works.
Unclear data contamination, missing baselines, or evaluation that cannot be reproduced.
Claims about safety, fairness, health, or society without matching evidence and limitations.
Formatting, anonymity, dual-submission, external-link, or supplement violations under the current-year policy.
A contribution framed for a neighboring field while giving ICLR reviewers too little technical or empirical substance.
Re-routing decision
If the paper misses ICLR's bar, compare against neural-information-processing-systems / international-conference-on-machine-learning / aaai-conference-on-artificial-intelligence / international-joint-conference-on-artificial-intelligence. Re-route based on contribution type, not prestige: theory to a theory venue, systems to a systems venue, application-heavy work to a domain venue, and early ideas to workshops or shorter tracks when the official CFP supports them.
Output format
[Fit] High / Medium / Low (one-line reason)
[Target] International Conference on Learning Representations (ICLR)
[Contribution type] algorithm / theory / system / dataset / benchmark / empirical / design / security / other
[Main evidence gap] <single most important missing proof, experiment, study, artifact, or policy check>
[Official items to re-check] CFP / author kit / deadline / format / anonymity / ethics / AI-use / artifact / rebuttal / camera-ready
[Top rejection risk] <venue-specific risk>
[Re-route suggestion] <better-matched conference or journal if not a fit>
Evaluates manuscript fit for the International Conference on Machine Learning (ICML) venue, including scope, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks.
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.