The rise of AI systems powered by data mining and machine learning has transformed industries and daily life. Yet, real-world deployment poses challenges due to biased data, oversimplified objectives, and limited robustness. As AI systems increasingly influence critical decisions, there is an urgent need to enhance their adaptability, reliability, and responsibility. The ARRL workshop brings together researchers and practitioners to advance foundational theory, algorithms, and practical frameworks for:

    1. Adaptable systems that evolve with changing tasks, environments, and societal needs;

    2. Reliable systems that remain robust under uncertainty and dynamic conditions;

    3. Responsible systems that promote trustworthy, sustainability, and societal outcomes.

We welcome submissions on novel research, algorithms, frameworks, and systems, including late-breaking results and impactful tools addressing ARR challenges.

Submission Guidelines

Paper submissions should be no longer than 6 pages, including the bibliography and any possible appendices. The acceptance format of any submission will be determined by the originality, significance, clarity, and scientific merit, depending on the reviews of Program Committee.

All submissions must be *single-blind*, in PDF format, and formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (in the IEEE two-column format). Template guidelines: https://www.ieee.org/conferences/publishing/templates.html. 

Paper Submission Website: https://wi-lab.com/cyberchair/2025/icdm25/scripts/submit.php?subarea=S45

List of Topics

We encourage submissions in various degrees of progress, such as new results, visions, techniques, innovative application papers, and progress reports under the topics that include, but are not limited to, the following broad categories:

1) 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

2) Reliable Learning:

• Robustness and Generalization in Data Mining
• Trustworthiness in Learning-enabled Systems
• Noise Handling and Outlier/Anomly Detection
• Data Wrangling and Munging for Reliable Preprocessing
• Data Quality Assessment and Assurance
• Robustness in Graph and Network Mining
• Uncertainty Quantification 
• Learning with Very Few Examples
• Open-World Learning 
• Learning with Unknown Unknowns

3) Responsible Learning:

• Explainable AI
• Efficient Data Mining
• Interpretability of Learning Results
• Privacy-Preserving Data Mining
• Ethical Data Mining and Data Usage
• Socio-technical Aspects of Data Mining
• Data Mining for Energy Efficiency

Organizing Committees

• Chen Zhao, Baylor University
• Yi He, William & Mary
• Xingquan (Hill) Zhu, Florida Atlantic University

Publication

ARRL-2025 proceedings will be published in the IEEE ICDMW Conference Proceedings, published by the IEEE Computer Society Press and indexed in IEEE Xplore. To be eligible, at least one author must complete a fullregistration for the conference.

Contact

All questions about submissions should be emailed to yihe@wm.edu