Call for Papers

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

This year we are running two publication tracks: Authors may submit either to the conference track, for which the proceedings will be published as a volume of Proceedings of Machine Learning Research Workshop and Conference Proceedings (PMLR), or to the journal track for which accepted papers will appear in a special issue of the Springer journal Machine Learning (MLJ).

Please note that submission arrangements for the two tracks are different.

Conference Track

Submission Information and Deadline: Will be announced soon

Manuscripts must be written in English, be a maximum of 16 pages (including references, appendices etc.) and follow the PMLR style. If required, supplementary material may be submitted as a separate file, but reviewers are not obliged to consider this.

The Latex camera-ready template and style file (updated in Sept 2021) can be found here: link.

All conference track submissions must be anonymized. Submissions that are not anonymized, over-length, or not in the correct format will be rejected without review. 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. 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.

After the acceptance notification, we plan to invite some of the selected authors to submit their extended papers to the Topical Issue in Springer Nature Computer Science.

Journal Track

Submission Information and Deadline: Will be announced soon.

In addition to the Conference Track, the Asian Conference on Machine Learning will be running a Journal Track. In order to ensure an efficient reviewing process, we encourage submissions of papers up to 20 pages. Papers that are accepted to the journal track must be presented at the conference in order to be published. 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.

Important Dates

Deadlines will be announced soon.

Conference Track

will be announced soon.

Journal Track

will be announced soon.

Topics

Topics of interest include but are not limited to:

  • General machine learning
    • Active learning
    • Bayesian machine learning
    • Dimensionality reduction
    • Feature selection
    • Graphical models
    • Imitation Learning
    • Latent variable models
    • Learning for big data
    • Learning from noisy supervision
    • Learning in graphs
    • Multi-objective learning
    • Multiple instance learning
    • Multi-task learning
    • Online learning
    • Optimization
    • Reinforcement learning
    • Relational learning
    • Semi-supervised learning
    • Sparse learning
    • Structured output learning
    • Supervised learning
    • Transfer learning
    • Unsupervised learning
    • Other machine learning methodologies
  • Deep learning
    • Attention mechanism and transformers
    • Deep learning theory
    • Generative models
    • Deep reinforcement learning
    • Architectures
    • Other topics in deep learning
  • Theory
    • Computational learning theory
    • Optimization (convex, non-convex)
    • Reproducible research
    • Bandits
    • Statistical learning theory
    • Other theories
  • Trustworthy Machine Learning
    • Accountability/Explainability/Transparency
    • Causality
    • Fairness
    • Privacy
    • Robustness
    • Other topics in trustworthy ML
  • Applications
    • Bioinformatics
    • Biomedical informatics
    • Collaborative filtering
    • Computer vision
    • COVID-19 related research
    • Healthcare
    • Human activity recognition
    • Information retrieval
    • Natural language processing
    • Social networks
    • Web search
    • Climate science
    • Other applications