ARTMAN '25: Third Workshop on Recent Advances in Resilient and Trustworthy MAchine learning-driveN systems

The ARTMAN workshop aims to bring together academic researchers and industry practitioners from diverse domains, primarily security & privacy and machine learning, but also various application fields, to collaboratively explore and discuss resilient and trustworthy machine learning-powered applications and systems. This workshop focuses on AI/ML application domains and welcomes contributions on both foundational and applied aspects of ML across various industries, including transportation, aerospace, healthcare, energy, and finance, among others, showcasing AI-driven advances in performance and efficiency. This workshop also seeks contributions on the application of reliable and secure AI/ML algorithms, especially knowledge-informed approaches, to improve resilience and trust, particularly in human-machine partnerships and interactions within such scenarios.

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Publication Details

Published
2025-11-19
DOI
https://doi.org/10.1145/3719027.3767660
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

ARTMAN '25: Third Workshop on Recent Advances in Resilient and Trustworthy MAchine learning-driveN systems

Takeshi Takahashi, Grégory Blanc, Zonghua Zhang
Adversarial Robustness in Machine Learning
article

ARTMAN '25: Third Workshop on Recent Advances in Resilient and Trustworthy MAchine learning-driveN systems

Takeshi Takahashi, Grégory Blanc, Zonghua Zhang
article en

Abstract

The ARTMAN workshop aims to bring together academic researchers and industry practitioners from diverse domains, primarily security & privacy and machine learning, but also various application fields, to collaboratively explore and discuss resilient and trustworthy machine learning-powered applications and systems. This workshop focuses on AI/ML application domains and welcomes contributions on both foundational and applied aspects of ML across various industries, including transportation, aerospace, healthcare, energy, and finance, among others, showcasing AI-driven advances in performance and efficiency. This workshop also seeks contributions on the application of reliable and secure AI/ML algorithms, especially knowledge-informed approaches, to improve resilience and trust, particularly in human-machine partnerships and interactions within such scenarios.

Institut Polytechnique de Paris (FR), Computer Science Laboratory of Lille (FR), National Institute of Information and Communications Technology (JP)
Openalex Percentile: Top 81%
Adversarial Robustness in Machine Learning
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ARTMAN '25: Third Workshop on Recent Advances in Resilient and Trustworthy MAchine learning-driveN systems — Takeshi Takahashi, Grégory Blanc, et al. · (2025) | TGRS Research Map | TGRS