Workshop program

Thursday, Sep. 14, 2017
9:15am
Welcome   [pdf]  
Patrick McDaniel
9:30am
Adversarial Machine Learning for Security and Privacy   [pdf]  
Ian Goodfellow, Google Brain
10:15am
Provably Secure Machine Learning   [pdf]  
Jacob Steinhardt, Stanford University
10:45am
Break
11:00am
Gradient Masking in Machine Learning   [pdf]  
Nicolas Papernot, Penn State University
11:30am
Adversarial Robustness via Optimization Lens   [pdf]  
Aleksander Madry, MIT
12:00pm
The Army Research Lab   [pdf]  
Tian Pham, ARL
12:30pm
Lunch
2:00pm
Breakout I  
(1) Measuring and achieving resilience, (2) Privacy, (3) Fairness
2:30pm
Breakout II   [pdf]  
(1) Attacks on training, (2) Adversarial reinforcement learning, (3) Autonomous cyber defense
3:00pm
Break
3:30pm
AI and Security: Lessons, Challenges and Future Directions   [pdf]  
Dawn Song
4:00pm
Shrinking and Exploring Adversarial Search Spaces   [pdf]  
David Evans, University of Virginia
4:30pm
End