Agent Skills
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international-conference-on-learning-representations
GitHub针对ICLR投稿的辅助技能，评估论文契合度、重构叙事框架、诊断证据缺口及规避拒稿风险。适用于确定目标为ICLR、需调整期刊/预印本风格或检查匿名与反驳策略的场景。
Trigger Scenarios
作者指定ICLR为目标会议
代表学习领域论文提交前评估
需将期刊或arXiv风格重构为CS会议叙事
需要诊断证据、匿名、成果或反驳策略
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill international-conference-on-learning-representations -g -y
SKILL.md
Frontmatter
{
"name": "international-conference-on-learning-representations",
"description": "Use when targeting International Conference on Learning Representations (ICLR) or deciding whether a computer-science manuscript fits this venue. Encodes conference fit, framing, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks for AI\/ML flagship."
}
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.
- Distinctive fingerprint for reviewer calibration: representation, learning, deep, generative, models, optimization, open-review, discussion, venue-specific, contribution, flagship, iclr.
- 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.
ICLR-specific routing detail
- Prefer ICLR when the contribution is representation learning, deep-learning architecture, optimization, generative modeling, self-supervision, interpretability, or learning dynamics with strong conceptual framing.
- Route statistically grounded ML theory/estimation to AISTATS/COLT/UAI, broad ML methods to ICML/NeurIPS, and systems/infrastructure work to MLSys when deployment mechanics dominate.
- ICLR evidence should show why the representation or learning mechanism matters, with ablations, controlled comparisons, robustness checks, and clear failure analysis.
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.
Official-cycle checklist
- Open the live official venue page: https://iclr.cc/
- 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>
Version History
- 1839142 Current 2026-07-05 12:45
