Topics of Interest
We welcome original and well-grounded research papers on all aspects of foundations of data science including but not limited to the following topics:
- Machine Learning Foundations for Data Science
- Auto-ML
- Information fusion from disparate sources
- Feature engineering, embedding, mining and representation
- Learning from network and graph data
- Learning from data with domain knowledge
- Reinforcement learning
- Non-IID learning, nonstationary, coupled and entangled learning
- Heterogeneous, mixed, multimodal, multi-view and multi-distributional learning
- Online, streaming, dynamic and real-time learning
- Causality and learning causal models
- Multi-instance, multi-label, multi-class and multi-target learning
- Semi-supervised and weakly supervised learning
- Representation learning of complex interactions, couplings, relations
- Deep learning theories and models
- Evaluation of data science systems
- Open domain/set learning
- Emerging Impactful Machine Learning Applications
- Data preprocessing, manipulation and augmentation
- Autonomous learning and optimization systems
- Digital, social, economic and financial (finance, FinTech, blockchains and cryptocurrencies) analytics
- Graph and network embedding and mining
- Machine learning for recommender systems, marketing, online and e-commerce
- Augmented reality, computer vision and image processing
- Risk, compliance, regulation, anomaly, debt, failure and crisis
- Cybersecurity and information disorder, misinformation/fake detection
- Human-centered and domain-driven data science and learning
- Privacy, ethics, transparency, accountability, responsibility, trust, reproducibility and retractability
- Fairness, explainability and algorithm bias
- Green and energy-efficient, scalable, cloud/distributed and parallel analytics and infrastructures
- IoT, smart city, smart home, telecommunications, 5G and mobile data science and learning
- Government and enterprise data science
- Transportation, manufacturing, procurement, and Industry 4.0
- Energy, smart grids and renewable energies
- Agricultural, environmental and spatio-temporal analytics and climate change
Contributions must contain new, unpublished, original and fundamental work relating to the Machine Learning Journal's mission. All submissions will be reviewed using rigorous scientific criteria whereby the novelty of the contribution will be crucial.
Submission Instructions
Submit manuscripts to: http://MACH.edmgr.com. Select this special issue as the article type. Papers must be prepared in accordance with the Journal guidelines: https://www.springer.com/journal/10994
All papers will be reviewed following standard reviewing procedures for the Journal.
Key Dates
We will have a continuous submission/review process starting in Oct. 2021.
Last paper submission deadline: 1 March 2022
Paper acceptance: 1 June 2022
Camera-ready: 15 June 2022
Guest Editors
Longbing Cao, University of Technology Sydney, Australia
João Gama, University of Porto, Portugal
Nitesh Chawla, University of Notre Dame, United States
Joshua Huang, Shenzhen University, China