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ARRL 2023 : International Workshop on Adaptable, Reliable, and Responsible Learning

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Link: https://arrl2023.github.io/home/
 
When Dec 1, 2023 - Dec 2, 2023
Where Shanghai, China
Abstract Registration Due Oct 15, 2023
Submission Deadline Jul 1, 2023
Notification Due Sep 1, 2023
Final Version Due Oct 15, 2023
Categories    data mining   machine learning   artificial intelligence   safe ai
 

Call For Papers

CALL FOR PAPERS - ARRL 2023 with IEEE ICDM'23
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International Workshop on Adaptable, Reliable, and Responsible Learning (ARRL)
December 1-2, 2023
Shanghai, China
https://arrl2023.github.io/home/
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For years, machine Learning has advanced artificial intelligence (AI) by enabling the development of systems that generate models from various databases without explicit instruction. The growing availability of data across various fields has led to the proliferation of learning-enabled systems, which embed machine learning components in the core, that have become increasingly powerful and integral to industry and everyday life. Data mining techniques allow such systems to examine vast quantities of data, identifying subtle features that often elude human capabilities. However, these techniques frequently rely on oversimplified learning objectives and data that may be biased, incomplete, or even hazardous. The transition from learning-enabled systems into real-world decision-making contexts thus can pose risks, primarily due to their limited adaptability, reliability, and responsibility in dealing with unfamiliar or unknown circumstances.

The inaugural International Workshop on Adaptable, Reliable, and Responsible Learning (ARRL) aims to gather researchers and practitioners to present recent advancements in addressing the three key aspects of learning within the context of data-driven and data-centric systems: adaptability, reliability, and responsibility. The workshop will explore theoretical foundations, algorithm designs, and frameworks that ensure future learning-enabled systems are

      1) *Adaptable*, by exhibiting evolvability with changes in the environment, societal dynamics, and task objectives or requirements, ensuring that the system remains relevant and effective in addressing diverse and dynamic challenges while maintaining high-performance standards;
      2) *Reliable*, by demonstrating robustness and stability in the presence of uncertainty, variability, and unknown unknowns, ensuring system safety and performance consistency across diverse conditions and high-stakes operating environments; and
      3) *Responsible*, by promoting sustainability, fairness, explainability, and trustworthiness in learning processes and outcomes, addressing ethical and privacy concerns and championing technology use for positive societal impact including solutions for affordable clean energy and climate action.

This workshop cordially invites submissions that showcase cutting-edge advances in research and development of adaptable, reliable, and responsible (ARR) learning algorithms and designs, as well as
late-breaking research that introduces published work or software that address ARR challenges and provide significant value to the community.



**** TOPICS ****
Topics of interest include, but are not limited to:

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 and Confidence Estimation in Learning-enabled Systems
* Learning with Very Few Examples
* Open-World Learning (Learning in Unexpected/Unknown Environments)



3) Responsible Learning:

* Explainable Learning Modules and Architectures
* Interpretability of Learning Results
* Algorithmic Fairness in Data Mining
* Discrimination-aware Data Mining
* Privacy-Preserving Data Mining
* Ethical Data Mining and Data Usage
* Socio-technical Aspects of Data Mining
* Bias Detection and Mitigation in Learning-enabled Systems
* AI for Environmental and Social Sustainability
* Data Mining for Energy Efficiency and Climate Action




**** SUBMISSION GUIDELINES ****
Paper submissions should be no longer than 10 pages (Long paper) or 6 pages (Short paper), in the IEEE 2-column format , including the bibliography and any possible appendices. Submissions longer than 10 pages will be rejected without review. There are no separate Long or Short paper tracks during submission. The acceptance format of any submission will be determined by the originality, significance, clarity, and scientific merit, depending on the reviews of Program Committee.

Accepted papers will be published in the ICDMW conference proceedings by the IEEE Computer Society Press.

A selected number of accepted papers will be invited for possible inclusion, in an expanded and revised form, in the xxx Journal

Manuscripts must be submitted electronically in online submission system. We do not accept email submissions.

Online Submission Site: https://wi-lab.com/cyberchair/2023/icdm23/scripts/submit.php?subarea=S33&undisplay_detail=1&wh=/cyberchair/2023/icdm23/scripts/ws_submit.php



**** LATEX AND WORD TEMPLATES ****
To help ensure correct formatting, please use the style files for U.S. Letter as template for your submission. These include LaTeX and Word.
IEEE Templates: https://www.ieee.org/conferences/publishing/templates.html


**** KEY DATES ****
(All deadlines are at 11:59PM Beijing Time)

· Paper submission (abstract and full paper): July 1st, 2023

· Notification of acceptance/rejection: September 1st, 2023

· Camera-ready deadline and copyright forms: October 15, 2023

· Early Registration Deadline: October 15, 2023

· Conference: December 1-2, 2023

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