2 citations · 4 across the 4 of their papers we have counts for
8 papers
Distributed Online Non-convex Optimization with Composite Regret
Zhanhong Jiang, Aditya Balu, Xian Yeow Lee +3
Regret has been widely adopted as the metric of choice for evaluating the performance of online optimization algorithms for distributed, multi-agent systems. However, data/model va…
Stochastic Conservative Contextual Linear Bandits
Jiabin Lin, Xian Yeow Lee, Talukder Jubery +3
Many physical systems have underlying safety considerations that require that the strategy deployed ensures the satisfaction of a set of constraints. Further, often we have only pa…
Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning Agents
Xian Yeow Lee, Yasaman Esfandiari, Kai Liang Tan +1
Advances in computing resources have resulted in the increasing complexity of cyber-physical systems (CPS). As the complexity of CPS evolved, the focus has shifted from traditional…
Robustifying Reinforcement Learning Agents via Action Space Adversarial Training
Kai Liang Tan, Yasaman Esfandiari, Xian Yeow Lee +2
Adoption of machine learning (ML)-enabled cyber-physical systems (CPS) are becoming prevalent in various sectors of modern society such as transportation, industrial, and power gri…
Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
Xian Yeow Lee, Sambit Ghadai, Kai Liang Tan +2
Robustness of Deep Reinforcement Learning (DRL) algorithms towards adversarial attacks in real world applications such as those deployed in cyber-physical systems (CPS) are of incr…
Learning to Cope with Adversarial Attacks
Xian Yeow Lee, Aaron Havens, Girish Chowdhary +1
The security of Deep Reinforcement Learning (Deep RL) algorithms deployed in real life applications are of a primary concern. In particular, the robustness of RL agents in cyber-ph…