ACML 2026 — 18th Asian Conference on Machine Learning
by Asian Conference on Machine Learning (ACML)
Asia-Pacific's flagship machine learning research conference, held in Melbourne the week before NeurIPS
About This Event
ACML 2026, the 18th Asian Conference on Machine Learning, takes place December 1–4, 2026 in Melbourne, Australia, immediately preceding NeurIPS 2026 in Sydney. It is the leading Asia-Pacific forum for machine learning research, covering general and deep learning, generative AI, learning theory, datasets, trustworthy ML, knowledge-intensive systems and applications in bioinformatics, climate science, computer vision, healthcare, NLP and scientific discovery. Tutorials and workshops open the programme on December 1. ACML runs three submission routes: a double-blind conference track on OpenReview with a 16-page limit, a journal track of up to 20 pages submitted to both OpenReview and Springer Nature, and a rolling Encore track that stays open until October 18, 2026. The conference is ranked CCF C and CORE C and is DBLP-indexed.
Key Dates & Deadlines
- Journal track final camera-ready
At a Glance
- Date
- December 1–4, 2026
- Location
- Melbourne, Australia
- Format
- In-Person
- Event Type
- Conference
- Status
- Upcoming
- Organizer
- Asian Conference on Machine Learning (ACML)
- Added
- Sep 11, 2026
- Updated
- Sep 11, 2026
Topics & Focus Areas
Who Should Attend
- ✓Machine learning researchers
- ✓Applied scientists and ML engineers
- ✓Heads of AI research labs
- ✓Data science leaders in Asia-Pacific
- ✓Graduate students and postdocs in ML
- ✓University and industry research recruiters
Why Attend
For an AI research director or APAC data science leader, ACML is a cost-effective way to recruit and benchmark: it gathers the Asia-Pacific machine learning research community in Melbourne the week before NeurIPS, when many of the field's senior researchers are already travelling to Australia. Its applied tracks in healthcare, climate and scientific discovery show which methods are moving from paper to practice, and the smaller scale makes one-to-one conversations with authors far easier than at NeurIPS itself.
Event Features
- ✓Conference track (double-blind, OpenReview, 16-page limit)
- ✓Journal track with Springer Nature (20-page limit, dual submission to OpenReview and Springer)
- ✓Encore track (rolling submissions until 18 Oct 2026)
- ✓Tutorials and workshops day, 1 December 2026
- ✓Call-for-papers areas: generative AI, deep learning, theory, datasets, trustworthy ML, knowledge-intensive systems
- ✓Application areas: bioinformatics, climate science, computer vision, healthcare, NLP, scientific discovery
What Makes This Unique
- ★Scheduled in Melbourne the week before NeurIPS 2026 in Sydney, so international attendees can cover both in one trip
- ★A journal track that routes accepted work through Springer Nature alongside the conference proceedings
- ★An Encore track with rolling submissions to 18 October 2026, for work already published elsewhere
- ★The main machine learning venue anchored in the Asia-Pacific research community
Industries Represented
Pricing Details
Registration fees have not yet been published on the ACML 2026 website. At least one author of each accepted paper must present at the conference.
Organizer
Asian Conference on Machine Learning (ACML)
acml-conf.org/2026/Frequently Asked
- When is ACML 2026?
- ACML 2026 takes place December 1–4, 2026 in Melbourne, Australia.
- Where is ACML 2026 held?
- ACML 2026 is held in Melbourne, Australia. The event format is in-person.
- What is the ACML 2026 submission deadline?
- The next ACML 2026 deadline is journal track final camera-ready on Dec 25, 2026.
- Who should attend ACML 2026?
- ACML 2026 is aimed at Machine learning researchers, Applied scientists and ML engineers, Heads of AI research labs, Data science leaders in Asia-Pacific, Graduate students and postdocs in ML, University and industry research recruiters.
- What topics does ACML 2026 cover?
- ACML 2026 covers Deep learning, Generative AI, Machine learning theory, Trustworthy machine learning, Reinforcement learning, Natural language processing, Computer vision, ML for healthcare, climate and scientific discovery.
Sources
This page was written from 3 sources, 1 on domains other than acml-conf.org.
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