MA4b.2

Adversarial Inverse Learning of Defense Policies Conditioned on Human Factor Models

Amirhossein Ravari, Northeastern University, United States; Guangyu Jiang, Zuyuan Zhang, George Washington University, United States; Mahdi Imani, Northeastern University, United States; Robert H. Thomson, Aryn A. Pyke, Nathaniel D. Bastian, United States Military Academy, West Point, United States; Tian Lan, George Washington University, United States
Session:
MA4b: Trustworthy Learning I Lecture
Track:
Adaptive Systems, Machine Learning, and Data Analytics
Location:
Nautilus
Presentation Time:
Mon, 28 Oct, 10:40 - 11:05 PT (UTC -8)
Session Chair:
Weiyu Xu, University of Iowa
Presentation
Discussion
Resources
No resources available.
Session MA4b
MA4b.1: Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning
Jirong Yi, Raghu Mudumbai, Weiyu Xu, University of Iowa, United States
MA4b.2: Adversarial Inverse Learning of Defense Policies Conditioned on Human Factor Models
Amirhossein Ravari, Northeastern University, United States; Guangyu Jiang, Zuyuan Zhang, George Washington University, United States; Mahdi Imani, Northeastern University, United States; Robert H. Thomson, Aryn A. Pyke, Nathaniel D. Bastian, United States Military Academy, West Point, United States; Tian Lan, George Washington University, United States
MA4b.3: Learning Fair Robustness via Domain Mixup
Meiyu Zhong, Ravi Tandon, University of Arizona, United States
MA4b.4: Causal Discovery with Unequal Edge Error Tolerance
Joni Shaska, Urbashi Mitra, University of Southern California, United States
Contacts