Theory, methodology, and resource papers are welcome from any of the following areas, including but not limited to:

Adaptable Learning

Online/Incremental Learning
Transfer Learning and Domain Adaptation
Lifelong/Continual/Meta Learning
Learning from Heterogeneous and Multi-Modal Data
Knowledge Discovery from Multiple Databases
Learning with Rejection/Abstention
Cross-Domain Data Mining
Evolving Data Stream Mining
Ensemble Learning in Dynamic Environments

Reliable Learning

Robustness and Generalization in Data Mining
Trustworthiness in Learning-enabled Systems
Noise Handling and Outlier/Anomaly Detection
Data Wrangling and Munging for Reliable Preprocessing
Data Quality Assessment and Assurance
Robustness in Graph and Network Mining
Uncertainty Quantification and Confidence Estimation in Learning-enabled Systems
Learning with Very Few Examples
Open-World Learning (Learning in Unexpected/Unknown Environments)

Responsible Learning

Explainable Learning Modules and Architectures
Interpretability of Learning Results
Algorithmic Fairness in Data Mining
Discrimination-aware Data Mining
Privacy-Preserving Data Mining
Ethical Data Mining and Data Usage
Socio-technical Aspects of Data Mining
Bias Detection and Mitigation in Learning-enabled Systems
AI for Environmental and Social Sustainability
Data Mining for Energy Efficiency and Climate Action