This conference will expand on the theoretical and practical understandings of vulnerabilities inherent to ML systems, explore the robustness of ML algorithms and systems, and aid in developing a unified, coherent scientific community which aims to build trustworthy ML systems.
Sponsor Type:1; 9
General Chair
Amy Hasan
Pennsylvania State University
Program Committee Chairs
Patrick McDaniel
Pennsylvania State University
Nicolas Papernot
University of Toronto and Vector Institute
Areas of Interest include (but are not limited to):
Trustworthy data curation
Novel attacks on ML systems
Methods for defending against attacks on ML systems
Forensic analysis of ML systems
Verifying properties of ML systems
Securely and safely integrating ML into systems
Privacy (e.g., confidentiality, inference privacy, machine unlearning)
Fairness
Accountability
Transparency
Interpretability
02月08日
2023
02月10日
2023
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