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.
Authors
- Takeshi Takahashi (ORCID: https://orcid.org/0000-0002-6477-7770)
- Grégory Blanc (ORCID: https://orcid.org/0000-0001-8150-6617)
- Zonghua Zhang (ORCID: https://orcid.org/0000-0003-0379-7089)
Institutions
- Institut Polytechnique de Paris (FR)
- Computer Science Laboratory of Lille (FR)
- National Institute of Information and Communications Technology (JP)
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