ACML 2025: Call for Papers

The 17th Asian Conference on Machine Learning (ACML 2025) will take place between December 9th - 12th, 2025, in Taipei, Taiwan. 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. Submissions from all parts of the world are encouraged.

At least one author of each accepted paper (for both tracks) must present the paper at the conference. Failure to do so will result in the paper not being published. All deadlines are at 23:59 AoE (Anywhere on Earth) unless otherwise specified.

New in 2025

To maintain high-quality peer review, each submission must nominate at least one author to serve as a reviewer, unless at least one author has served as an area chair. The nominated author should have published at least one first-author paper and at least one paper in top venues (e.g., ICML, NeurIPS, ICLR, ACML, AAAI, CVPR, ECCV, ICCV, ACL, EMNLP, NAACL, or equivalent). Submissions without this nomination will be desk-rejected. If no author meets the requirement, contact acml_2025_programchairs@googlegroups.com before 30 June 2025 with the OpenReview submission ID.

Submission Tracks

Conference Track

• Limit: 16-page limit with references and appendix.
• Proceedings: Published by Proceedings of Machine Learning Research Workshop and Conference Proceedings (PMLR).
• Submission Deadline: June 26, 2025 (Extended).
• Submission System: OpenReview
• Requirements: Manuscripts must be in English, follow the Latex submission template, and be anonymized for double-blinded review. Submissions that are not anonymized, over-length, or not in the correct format will be rejected without review. Papers must not be substantially similar to previously published or concurrently submitted works, but can be available as technical reports (e.g., arXiv) without citation.

Journal Track

• Limit: Maximum of 20 pages (including references, appendices, etc.).
• Proceedings: Published in the Machine Learning journal.
• Submission Deadline: May 30, 2025.
• Submission Systems:
  1. OpenReview (for title, abstract, and manuscript).
  2. Springer Nature's manuscript submission system (for the full manuscript, choose "Research" as article type and "ACML 2025" as collection type).
• Requirements: Manuscripts must be in English, follow the journal's submission guidelines. The review process is single-blind. Substantially similar or extended versions of previously published papers are not eligible.

Important Dates

Conference Track Dates

• Submission deadline: June 26, 2025
• Reviews released to authors: August 4, 2025
• Author rebuttal deadline: August 11, 2025
• Acceptance notification: September 1, 2025
• Camera-ready submission deadline: September 22, 2025

Journal Track Dates

• Submission deadline: May 30, 2025
• 1st round review results: July 14, 2025
• Revised manuscript submission deadline (minor revision): July 31, 2025
• 2nd round review results: August 15, 2025
• Acceptance notification: September 1, 2025
• Conditional Camera-Ready Manuscript Submission Deadline: September 22, 2025 (for presentation at the conference)
• Final Camera-Ready Manuscript Submission Deadline: December 25, 2025 (for manuscripts presented at ACML 2025)

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, Learning with noisy labels, Other machine learning methodologies.

Deep learning: Architectures, Deep reinforcement learning, Generative models, Multi-modality learning, 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, Adversarial learning, 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, ML for science, Other applications.

The Journey Track also welcomes overview or position papers.