Asian Conference On Machine Learning

Asian Conference on Machine Learning (ACML)

Conference positioning

Asian Conference on Machine Learning (ACML) is a top computer-science conference venue for machine learning methods, theory, and applications with strong Asia-Pacific community visibility. It rewards a rigorous ML contribution seeking a focused but international ML venue. 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 ACML / Asian Conference on Machine Learning as the target venue.
• A manuscript in machine learning methods 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: machine learning methods, theory, and applications with strong Asia-Pacific community visibility.
• 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 ACML'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: Treat ACML as a AI/ML regional flagship venue whose reviewers expect the scope and evidence to match its own community. Do not submit a generic CS paper until the introduction names the exact subcommunity, contribution type, and proof or empirical standard.
• Contribution hook to foreground: the venue-specific contribution bar.
• Scope vocabulary to use naturally in the abstract and introduction: machine learning methods, theory, and applications with strong Asia-Pacific community visibility.
• Distinctive fingerprint for reviewer calibration: machine, learning, methods, theory, applications, strong, asia-pacific, community, visibility, venue-specific, contribution, regional, flagship, acml-conf.
• Official anchor domain: www.acml-conf.org. Quote annual rules only after opening that source and the current-year CFP/author kit.

Close-neighbor routing guardrail

• Use this profile only when the manuscript's central contribution is genuinely in AI/ML regional flagship and the author can say why ACML reviewers are the primary audience, not merely a convenient deadline.
• Closest roster neighbors to compare before final routing: siam-international-conference-on- data-mining (SDM), european-conference-on-machine-learning-and-principles-and-practice-of- knowledge-discovery (ECML PKDD), the-web-conference (WWW), acm-international-conference- on-web-search-and-data-mining (WSDM). Break ties by contribution type, evidence shape, reviewer community, and the current official CFP from www.acml-conf.org.

Method & evidence bar

• Clone-audit guardrail: ACML is an ML venue, not a generic AI-application parking lot. Keep ACML when the paper advances a learnable objective, model class, inference procedure, generalization analysis, benchmark protocol, or ML application with reusable method insight. If the work is primarily health deployment, data-mining discovery, web search, automated tool construction, or lifelong-learning setting design, compare MLHC/KDD/WSDM/AutoML/CoLLAs before recommending ACML.
• 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 ACML, the evidence must support the venue-specific signature: a rigorous ML contribution seeking a focused but international ML venue.
• 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://www.acml-conf.org/
• 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 / CMT / HotCRP / PCS / START or society portal, as specified for the current cycle.
• 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 ACML, 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 regional 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 ACML reviewers too little technical or empirical substance.

Re-routing decision

If the paper misses ACML's bar, compare against neural-information-processing-systems / international-conference-on-machine-learning / international-conference-on-learning-representations / aaai-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] Asian Conference on Machine Learning (ACML)
[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>

Source: brycewang-stanford/Awesome-Journal-Skills → Computer-Science-Conference-Skills/skills/asian-conference-on-machine-learning/SKILL.md