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.

SubmissionPermalink
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 TrackPermalink
Submission Deadline: June 25

For the conference track please submit your manuscript via CMT at: https://cmt3.research.microsoft.com/ACML2021

When creating a new submission on CMT, please make sure to choose “Conference” track.

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.

Latex template and style files will be provided at a later date.

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 TrackPermalink
Submission Deadline: May 14

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.

For this year’s journal track, the abstract and the paper must be submitted to different systems for the purpose of review management.

First, please submit the abstract via CMT at: https://cmt3.research.microsoft.com/ACML2021

When creating a new submission on CMT, please make sure to choose “Journal” track. Then, please submit the paper via Springer’s Editorial Manager system at: https://www.editorialmanager.com/mach

When creating a new submission on Springer’s Editorial Manager, please make sure to choose “S.I. : ACML 2021” as the article type.

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 DatesPermalink
Deadlines are 23:59 Pacific Time (PST/PDT) for all dates.

Conference TrackPermalink
Date Event
25 June 2021 Submission Deadline
11 August - 25 August 2021 Author Rebuttal
03 September 2021 Acceptance Notification
22 September 2021 Camera-Ready Submission Deadline
Journal TrackPermalink
Date Event
14 May 2021 Submission Deadline
30 June 2021 1st Round Review Results (accept, minor revision, or reject)
13 August 2021 Revised Manuscript Submission Deadline
17 September 2021 Notification
15 October 2021 Camera-Ready Submission Deadline
TopicsPermalink
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