55 citations · 338 across the 26 of their papers we have counts for
6 papers · 1 filter
Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled Robotics and Autonomous Systems
Yi Dong, Xingyu Zhao, Sen Wang +1
Deep Reinforcement Learning (DRL) has achieved impressive performance in robotics and autonomous systems (RAS). A key challenge to its deployment in real-life operations is the pre…
SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability
Wei Huang, Xingyu Zhao, Gaojie Jin +1
Interpretability of Deep Learning (DL) is a barrier to trustworthy AI. Despite great efforts made by the Explainable AI (XAI) community, explanations lack robustness -- indistingui…
Demonstrating Software Reliability using Possibly Correlated Tests: Insights from a Conservative Bayesian Approach
Kizito Salako, Xingyu Zhao
This paper presents Bayesian techniques for conservative claims about software reliability, particularly when evidence suggests the software's executions are not statistically inde…
Short-term Load Forecasting with Distributed Long Short-Term Memory
Yi Dong, Yang Chen, Xingyu Zhao +1
With the employment of smart meters, massive data on consumer behaviour can be collected by retailers. From the collected data, the retailers may obtain the household profile infor…
The Unnecessity of Assuming Statistically Independent Tests in Bayesian Software Reliability Assessments
Kizito Salako, Xingyu Zhao
When assessing a software-based system, the results of Bayesian statistical inference on operational testing data can provide strong support for software reliability claims. For in…
Hierarchical Distribution-Aware Testing of Deep Learning
Wei Huang, Xingyu Zhao, Alec Banks +2
Deep Learning (DL) is increasingly used in safety-critical applications, raising concerns about its reliability. DL suffers from a well-known problem of lacking robustness, especia…