Asian Conference on Machine Learning (ACML)

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,