CALL FOR PAPERS

The 15th Asian Conference on Machine Learning (ACML 2023) will take place between November 11-14, 2023 in İstanbul, Turkey. 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.

The conference runs two publication tracks, authors may submit either to:
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).
Journal Track: (20-page limit with references) for which accepted papers will appear in a special issue of the Springer Machine Learning Journal (MLJ).
Please refer to http://www.acml-conf.org/2023/ for more details. Instructions for submission and LaTeX templates will be available soon.

IMPORTANT DATES
(subject to minor changes in case there are conflicts with timelines of other major ML conferences)

Conference Track
23 June 2023 Submission deadline
11 August 2023 Reviews released to authors
18 August 2023 Author rebuttal deadline
08 September 2023 Acceptance notification
29 September 2023 Camera-ready submission deadline
Journal Track
26 May 2023 Submission deadline
07 July 2023 1st round review results (accept, minor revision, or reject)
11 August 2023 Revised manuscript submission deadline (for minor revision papers)
08 September 2023 Acceptance notification
29 September 2023 Camera-ready submission deadline

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
Neuro-symbolic 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
Architectures
Attention mechanism and transformers
Deep learning theory

Deep reinforcement learning
Generative models
Supervised learning
Other topics in deep learning
Theory
Bandits
Computational learning theory
Game theory
Matrix/tensor methods
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
Other topics in trustworthy ML
Applications
Bioinformatics
Biomedical informatics
Climate science
Collaborative filtering
Computer vision
COVID-19 related research
Healthcare
Human activity recognition
Information retrieval
Natural language processing
Social good
Social networks
Web search
Other applications