Call for Papers

The 16th Asian Conference on Machine Learning (ACML 2024) will take place between December 5 - 7, 2024 in Hanoi, Vietnam. The conference aims to provide a leading international forum for researchers in machine learning and related fields to share their new ideas, progress and achievements.
The conference calls for high-quality, original research papers in the theory and practice of machine learning. The conference also solicits proposals focusing on frontier research, new ideas and paradigms in machine learning. We encourage submissions from all parts of the world, not only confined to the Asia-Pacific region.

Submission Instructions

Similar to previous years, ACML 2024 has two publication tracks. Each paper may be submitted to either:

Conference Track

Conference Track: (16-page limit with references) for which the proceedings will be published as a volume of Proceedings of Machine Learning Research Workshop and Conference Proceedings (PMLR).
Submission Deadline: 26 June 2024
For the conference track, please submit your manuscript via OpenReview at:
https://openreview.net/group?id=ACML.org/2024/Conference
Manuscripts must be written in English, and should follow the Latex submission template and style file here ACML2024_submission_template.zip with a 16-page limit, including references and appendix. Supplementary materials may be submitted as a separate file, but reviewers are not obliged to consider it.
All conference track submissions must be anonymized for double-blinded review. Submissions that are not anonymized, over-length, or not in the correct format will be rejected without review. To anonymize, simply leave the author information empty in the Tex template. There is no separate format for anonymizing.
It is not appropriate to submit papers that are substantially similar to versions that have been previously published, or accepted for publication, or that have been submitted in parallel to other conferences or journals (including our journal track). However, submission is permitted for papers presented or to be presented at conferences or workshops without proceedings, or with only abstracts published. Also, submission is permitted for papers that are available as a technical report (e.g., in arXiv) as long as it is not cited in the submission.

Journal Track

Submission Deadline: May 29, 2024
In addition to the conference track, this year’s ACML will run a journal track, similar to previous years. Papers that are accepted to the journal track must be presented at the conference in order to be published.
IMPORTANT: Similar to previous years, for the journal track, the abstract and the paper must be submitted to two different systems simultaneously for the purpose of review management:

1) First, please submit ONLY the title and abstract via OpenReview at ACML 2024 Journal Track | OpenReview: (paper manuscript need not be submitted here).
2) Then, please submit the full paper via Springer Nature’s manuscript submission system at: Select article type : Machine Learning (springernature.com). When creating a new submission on Springer’s Editorial Manager, please make sure to choose “Special Issue for ACML 2024” as the article type.

Failure to submit to both systems will result in desk-reject of the paper.

For the journal track, manuscripts must be written in English with a maximum of 20 pages (including references, appendices, etc.). For the template and style files, please follow the instructions for authors on the journal website: https://www.springer.com/computer/ai/journal/10994.

The journal track will follow the reviewing process of the Machine Learning journal. This includes allowing papers that require minor changes to be resubmitted after a first-round review. The journal track committee will aim to complete the reviewing process in time for this year’s conference. In the unlikely event that the reviewing process for a paper is not completed in time (for this year’s conference), the paper will not be considered for the conference and the review will be completed as a regular submission to the Machine Learning journal.

The journal track review is single-blind, i.e., the authors’ identity will be visible to reviewers. It is not appropriate to submit papers that are substantially similar to versions that have been previously published, or accepted for publication, or that have been submitted in parallel to other conferences or journals. Submissions that are not in the correct format will be rejected without review. In addition, extended versions of published conference papers are not eligible for journal track submission. However, submission is permitted for papers presented or to be presented at conferences or workshops without proceedings, or with only abstracts published. Also, submission is permitted for papers that are available as a technical report (e.g., in arXiv).

Important Dates

Kindly note that all deadlines would be at 23:59 AoE (Anywhere on Earth) unless otherwise specified.

Conference Track Dates

  • 26 June 2024: Submission deadline

  • 14 August 2024: Reviews released to authors

  • 21 August 2024: Author rebuttal deadline

  • 04 September 2024: Acceptance notification

  • 25 September 2024: Camera-ready submission deadline

Journal Track Dates

  • May 29 2024: Submission deadline

  • July 3 2024: 1st round review results (accept, minor revision, or reject)

  • August 7 2024: Revised manuscript submission deadline (for minor revision papers)

  • September 4 2024: Acceptance notification

  • September 29 2024: Camera-ready submission deadline

Topics

Topics of interest include but are not limited to:

  • General machine learning
    • Active learning
    • Bayesian machine learning
    • Clustering
    • Imitation Learning
    • Learning to Rank
    • Meta-Learning
    • Multi-objective learning
    • Multiple instance learning
    • Multi-task learning
    • Neuro-symbolic methods
    • Online learning
    • Optimization
    • Reinforcement learning
    • Relational learning
    • Self-supervised learning
    • Semi-supervised learning
    • Structured output learning
    • Supervised learning
    • Transfer learning
    • Unsupervised learning
    • Weakly-supervised learning
    • Other machine learning methodologies
  • Deep learning
    • Architectures
    • Deep reinforcement learning
    • Generative models
    • Large-language models and other foundation models
    • Deep learning theory
    • Other topics in deep learning
  • Theory
    • Bandits
    • Computational learning theory
    • Game theory
    • Optimization
    • Statistical learning theory
    • Other theories
  • Datasets and reproducibility
    • Implementations, libraries
    • ML datasets and benchmarks
    • Other topics in reproducible ML research
  • Trustworthy machine learning
    • Accountability, explainability, transparency
    • Causality
    • Fairness
    • Privacy
    • Robustness
    • AutoML
    • Other topics in trustworthy ML
  • Learning in knowledge-intensive systems
    • Knowledge refinement and theory revision
    • Multi-strategy learning
    • Other systems
  • Applications
    • Bioinformatics
    • Biomedical informatics
    • Climate science
    • Collaborative filtering
    • Computer vision
    • Healthcare
    • Human activity recognition
    • Information retrieval
    • Natural language processing
    • Social good
    • Social networks
    • Web search
    • Other applications