In continual learning, models can continually accumulate knowledge over time without the need to retrain from scratch, with particular methods aimed to alleviate forgetting. It can continually learn from a stream of experiential data, building on what was learnt previously, while being able to reapply, adapt and generalize to new situations. This is particularly important when there are changes in the data streams. Current predictive models need to be adapted to these changes (drifts) as soon as possible while maintaining good performance measures (e.g. accuracy, time, delay, energy efficiency).

The aim of this workshop is to bring together researchers from the areas of continual learning, model adaptation and concept drift in order to encourage discussions and new collaborations on solving the problems in this domain. We like to encourage state-of-the art research in the area of continual learning, model adaptation and concept drift. Beyond that we encourage research that demonstrates the applicability of these research in various areas including (but not limited to) earth and environmental science, sensor networks and transportation network. We encourage the submissions of research that incorporates the fundamentals of green AI. Therefore, this workshop encourages submissions that attempts to address any of these issues.

This workshop will provide a forum for international researchers and practitioners to share and discuss their original and interesting work on addressing new challenges and research issues in the area.

The topics of interest of this workshop include (but not limited to) the following:

• New data-level and algorithm-level approaches in non-stationary environments for continual learning, model adaptation and concept drift.
• Novel artificial neural networks approaches for continual learning, including meta-learning, sparse neural networks, and sparse training.
• Continual reinforcement learning
• Adaptive ensemble approaches for data streams.
• Passive and active approaches to dealing with concept drift.
• Approaches to dealing with recurring concepts.
• Semi-supervised learning and active learning approaches.
• Explainable AI (XAI) approaches for drift explanation. 
• Performance evaluation in incremental and online learning scenarios.
• Case studies and real-world applications.
• Green AI for data streams.