52 citations · 66 across the 11 of their papers we have counts for
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cs.LG2022★ 2 cited
Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising
Zikang Xiong, Joe Eappen, He Zhu +1
Neural network policies trained using Deep Reinforcement Learning (DRL) are well-known to be susceptible to adversarial attacks. In this paper, we consider attacks manifesting as p…
cs.LG2020★ 3 cited
Robustness to Adversarial Attacks in Learning-Enabled Controllers
Zikang Xiong, Joe Eappen, He Zhu +1
Learning-enabled controllers used in cyber-physical systems (CPS) are known to be susceptible to adversarial attacks. Such attacks manifest as perturbations to the states generated…
cs.LG2019★ 52 cited
An Inductive Synthesis Framework for Verifiable Reinforcement Learning
He Zhu, Zikang Xiong, Stephen Magill +1
Despite the tremendous advances that have been made in the last decade on developing useful machine-learning applications, their wider adoption has been hindered by the lack of str…