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About NMLTopics of interestKey dates



NEVERENDING
MACHINE LEARNING

NML 2024 is co-located with ICDM 2024, Abu Dhabi, UAE, December 9-12, 2024

The 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.


CONTINUAL AND LIFELONG MACHINE LEARNING

Techniques and algorithms that enable continual learning and adaptation over
time, preserving knowledge while accommodating new data and tasks.


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representations, allowing for both removal of outdated knowledge and adapting to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
   
   September 10, 2024

 * Author notification date
   
   October 7, 2024

 * Camera-ready deadline and copyright form
   
   October 11, 2024

 * Workshop date
   
   December 9, 2024

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).

Link to submission system coming soon.


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.
   
   * X
   * LinkedIn


 * PAWEŁ KSIENIEWICZ
   
   Department of Systems and Computer Networks, Wroclaw University of Science
   and Technology, Poland.
   
   * X
   * LinkedIn


 * BARTOSZ KRAWCZYK
   
   Center for Imaging Science, Rochester Institute of Technology, USA.
   
   * X
   * LinkedIn

About NMLTopics of interestKey dates

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