1 citations · 1 across the 1 of their papers we have counts for
3 papers
cs.AI2020
Verifiably Safe Exploration for End-to-End Reinforcement Learning
Nathan Hunt, Nathan Fulton, Sara Magliacane +3
Deploying deep reinforcement learning in safety-critical settings requires developing algorithms that obey hard constraints during exploration. This paper contributes a first appro…
cs.SE2020★ 1 cited
Formal Verification of End-to-End Learning in Cyber-Physical Systems: Progress and Challenges
Nathan Fulton, Nathan Hunt, Nghia Hoang +1
Autonomous systems -- such as self-driving cars, autonomous drones, and automated trains -- must come with strong safety guarantees. Over the past decade, techniques based on forma…
cs.CV2019
On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method
Pu Zhao, Sijia Liu, Pin-Yu Chen +4
Robust machine learning is currently one of the most prominent topics which could potentially help shaping a future of advanced AI platforms that not only perform well in average c…