Workshop on Recent Advances in Resilient and Trustworthy MAchine learniNg

Overview

This workshop aims at bringing together academic researchers and industrial practitioners from different domains with diverse expertise (mainly security & privacy and machine learning, but also from application domains) to collectively explore and discuss the topics about resilient and trustworthy machine learning-powered applications and systems, share their views, experiences, and lessons learned, and provide their insights and perspectives, so as to converge on a systematic approach to securing them.

Topics of Interest

Topics of interest include (but are not limited to):

• Threat modeling and risk assessment of ML systems and applications in intelligent systems, including, but not limited to, anomaly detection, failure prediction, root cause analysis, incident diagnosis
• Data-centric attacks and defenses of ML systems and applications in intelligent systems, such as model evasion via targeted perturbations in testing samples, data poisoning in training examples
• Adversarial machine learning, including adversarial examples of input data and adversarial learning algorithms developed for intelligent systems
• ML robustness: testing, simulation, verification, validation, and certification of robustness of ML pipelines (not only ML algorithms and models) in intelligent systems, including but not limited to data-centric analytics, model-driven methods, and hybrid methods
• AI system safety: dependability topics related to AI system development and deployment environments, including hardware, ML platform and framework, software
• Trust in AI systems and applications, this mainly explores the trust issues arising from the interactions between human users and AI systems (e.g., Man-Machine Symbiosis, Human-Machine Teaming), with a particular focus on interpretable, explainable, accountable, transparent, and fair AI systems and applications in intelligent systems
• Resilience by reaction: Leveraging AI/ML algorithms, especially knowledge-informed models, to improve resilience and trust of intelligent systems

Submission Guidelines

• Submissions should be 6-10 pages, using double-column ACM proceedings (acmart) template available here, with the [sigconf,anonymous] options. Two additional pages can be used for well-referenced appendices. Note that the reviewers are not expected to read these appendices.
• All submissions must be anonymous.
• Accepted workshop papers and slides will be published on the ACSAC website with open access
• Extended versions of selected papers may be considered for publication in a Special Issue.

Submission Link

EasyChair

Important Dates

• September 1, 2024: Submission Deadline
• October 6, 2024: Acceptance Notification
• November 3, 2024: Camera-Ready Paper Submission Deadline
• December 9, 2024: Workshop

Organizing Committee

Program Chairs

• Gregory Blanc (Telecom SudParis, Institut Polytechnique de Paris, France)
• Takeshi Takahashi (National Institute of Information and Communications Technology, Japan)
• Zonghua Zhang (Huawei Paris Research Center, France)

TPC Members (to be completed)

Sponsor

This workshop is co-located with the ACSAC 2024 conference and is partially supported by the GRIFIN project (ANR-20-CE39-0011).