ICDM 2021

Workshop on Continual Learning and Adaptation for Time Evolving Data

Second Edition

December 7, 2021, Auckland, New Zealand

About the workshop

About the workshop

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.


Invited Speakers

Razvan Pascanu
Eric Eaton
University of Pennsylvania
Vincenzo Lomonaco
University of Pisa


Paper submissions should be limited to a maximum of 8 pages plus 2 extra pages, in the IEEE 2-column format used by the IEEE ICDM 2021 conference, including the bibliography and any possible appendices. All submissions will be peer reviewed by the Program Committee on the basis of technical quality, relevance to scope of the conference, originality, significance, and clarity. Each submission should be regarded as an undertaking that, if the paper is accepted, at least one of the authors must register and present the work. By the unique ICDM tradition, all accepted workshop papers will be published in the dedicated ICDMW proceedings published by the IEEE Computer Society Press. Therefore, papers must not have been accepted for publication elsewhere or be under review for another workshop, conferences or journals.

Submit your paper here

Key Dates:
Submission deadline: September 3, 2021 September 15, 2021
Acceptance notification: September 24, 2021 September 30, 2021
Camera-ready deadline: October 1, 2021
Workshop date: December 7, 2021


Organizing Committee

Decebal Mocanu University of Twente, The Netherlands
Ghada Sokar Eindhoven University of Technology (TU/e), The Netherlands
Albert Bifet Télécom ParisTech, France and University of Waikato, New Zealand

Program Committee

Workshop Program