Continual and Lifelong Machine Learning
Techniques and algorithms that enable continual learning and adaptation over time, preserving knowledge while accommodating new data and tasks.
Neverending
Machine Learning
NML 2024 is co-located with ICDM 2024, Abu Dhabi, UAE, December 9-12, 2024The landscape of machine learning is evolving beyond the traditional paradigm where models are trained and tested on stationary datasets. Real-world applications increasingly demand adaptability to changing data distributions, continuous learning of new tasks, and the ability to handle evolving environments. The Neverending Machine Learning (NML) workshop aims to explore and advance techniques that enable lifelong learning, adaptive modeling, and robustness in the face of dynamic data scenarios.
Objective: The workshop aims to bring together researchers and practitioners interested in advancing the capabilities of machine learning systems beyond traditional static datasets. Participants will explore cutting-edge research, share insights, and discuss challenges and opportunities in developing truly adaptive and evolving machine learning solutions.
Format: The workshop will feature keynote presentations by leading experts, contributed paper presentations, and interactive panel discussions. Participants will have the opportunity to engage in hands-on sessions and collaborative activities aimed at fostering innovation and networking among attendees.
Target Audience: Researchers, practitioners, and students working in machine learning, artificial intelligence, data science, and related fields are encouraged to participate. Participants with expertise or interest in lifelong learning, adaptive systems, and dynamic data environments will find the workshop particularly relevant.
Topics of interest
What area of expertise are you interested in? Here are some of the topics we will be covering at the workshop.
Learning from High-speed Data Streams
Methods for learning from continuously arriving data, where traditional batch learning and fully supervised approaches are impractical.
Machine Unlearning
Techniques to remove specific knowledge or biases from a model's learned representations, allowing for both removal of outdated knowledge and adapting to evolving nature of data (such as changing privacy / ethical considerations).
Test-time Adaptation
Approaches that enable models to adapt their behavior during inference based on the specific characteristics of the input data or the environment, such as presence of concept drift.
Adaptive TinyML
Techniques and methodologies for implementing adaptive machine learning models on resource-constrained devices, enabling continuous learning and adaptation in edge computing scenarios.
Continual Multi-task Learning
Methods that allow models to learn multiple tasks simultaneously, leveraging shared knowledge and enhancing generalization.
Continual Transfer Learning
Approaches for transferring knowledge from one domain or task to another, improving learning efficiency and performance in new environments.
Continual Open-world Learning
Strategies to recognize and handle unknown classes or concepts during training and inference, ensuring models can operate effectively in open-world scenarios.
Out-Of-Distribution Detection
Techniques to identify data samples that do not belong to the training distribution, crucial for maintaining model reliability and safety.
Few-shot Learning
Algorithms capable of learning new concepts from a few labeled examples, mimicking human-like rapid learning abilities, especially in continual and lifelong learning scenarios.
Key dates
Paper submission deadline
Author notification date
All times are at 11:59PM AoE unless otherwise stated
Submission instructions
Authors are invited to submit original research contributions or position papers addressing one or more of the workshop`s topics. English-language research contributions that have not been concurrently submitted or published elsewhere. Submissions should adhere to the ICDM formatting and submission guidelines, i.e., they must adhere to the IEEE 2-column format. For the regular paper track, submissions should not exceed 8 pages of content, plus an additional 2 pages for references. For the short paper track, submissions should be limited to a maximum of 4 pages of content, plus 1 extra page for references.
In alignment with the ICDM 2024 reviewing scheme, all submissions will undergo triple-blind reviews by the Program Committee, evaluating technical quality, relevance to the conference scope, originality, significance, and clarity. All accepted papers will be presented as posters, with a select few chosen for oral presentations. A best paper award will be conferred. Accepted papers will be published in the IEEE ICDM 2024 Workshop proceedings (published by IEEE and EI-indexed).
Organizers
For inquiries regarding the workshop, please contact michal.wozniak@pwr.edu.pl and bartosz.krawczyk@rit.edu.
Michał Woźniak
Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Poland.
Paweł Ksieniewicz
Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Poland.
Bartosz Krawczyk
Center for Imaging Science, Rochester Institute of Technology, USA.
