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