Machine Learning - CFP: Special Issue on ACML 2024
Special issue on ACML 2024
The 16th Asian Conference on Machine Learning (ACML 2024) will take place between December 5 - 7, 2024. 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.
Submission Instructions
The conference features a journal track, for which accepted papers will appear in a special issue of the Springer Machine Learning Journal (MLJ).
- Journal Track: (20-page limit with references) for which accepted papers will appear in a special issue of the Springer Machine Learning Journal (MLJ).
Important 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 methodologies
- 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 methods
- 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
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
- COVID-19 related research
- Healthcare
- Human activity recognition
- Information retrieval
- Natural language processing
- Social good
- Social networks
- Web search
- Other applications
Guest Editors
Kee-Eung Kim, Korea Advanced Institute of Science and Technology, keeeung.kim@kaist.edu
Shou-De Lin, Național Taiwan University, sdlin@csie.ntu.edu.tw