4 papers
A Formal Framework for Predicting Distributed System Performance under Faults (Extended Version)
Ziwei Zhou, Si Liu, Zhou Zhou +2
Today's distributed systems operate in complex environments that inevitably involve faults and even adversarial behaviors. Predicting their performance under such environments dire…
Quantitative Verification of Omega-regular Properties in Probabilistic Programming
Peixin Wang, Jianhao Bai, Min Zhang +1
Probabilistic programming provides a high-level framework for specifying statistical models as executable programs with built-in randomness and conditioning. Existing inference tec…
Unifying Qualitative and Quantitative Safety Verification of DNN-Controlled Systems
Dapeng Zhi, Peixin Wang, Si Liu +2
The rapid advance of deep reinforcement learning techniques enables the oversight of safety-critical systems through the utilization of Deep Neural Networks (DNNs). This underscore…
Robustness Verification of Deep Reinforcement Learning Based Control Systems using Reward Martingales
Dapeng Zhi, Peixin Wang, Cheng Chen +1
Deep Reinforcement Learning (DRL) has gained prominence as an effective approach for control systems. However, its practical deployment is impeded by state perturbations that can s…