Call for Papers
Areas of interest
IEEE SaTML expands upon the theoretical and practical understandings of vulnerabilities inherent to machine learning (ML), explores the robustness of learning algorithms and systems, and aids in developing a unified, coherent scientific community aiming to establish trustworthy machine learning. Topics of interest include (but are not limited to):
- Novel attacks on machine learning
- Novel defenses for machine learning
- Secure and safe machine learning in practice
- Verification of algorithms and systems
- Machine learning system security
- Privacy in machine learning
- Forensic analysis of machine learning
- Fairness and interpretability
- Trustworthy data curation