10 citations · 74 across the 31 of their papers we have counts for
9 papers · 1 filter
Testing learning-enabled cyber-physical systems with Large-Language Models: A Formal Approach
Xi Zheng, Aloysius K. Mok, Ruzica Piskac +5
The integration of machine learning (ML) into cyber-physical systems (CPS) offers significant benefits, including enhanced efficiency, predictive capabilities, real-time responsive…
Repairing Learning-Enabled Controllers While Preserving What Works
Pengyuan Lu, Matthew Cleaveland, Oleg Sokolsky +2
Learning-enabled controllers have been adopted in various cyber-physical systems (CPS). When a learning-enabled controller fails to accomplish its task from a set of initial states…
Distributionally Robust Statistical Verification with Imprecise Neural Networks
Souradeep Dutta, Michele Caprio, Vivian Lin +5
A particularly challenging problem in AI safety is providing guarantees on the behavior of high-dimensional autonomous systems. Verification approaches centered around reachability…
Causal Repair of Learning-enabled Cyber-physical Systems
Pengyuan Lu, Ivan Ruchkin, Matthew Cleaveland +2
Models of actual causality leverage domain knowledge to generate convincing diagnoses of events that caused an outcome. It is promising to apply these models to diagnose and repair…
Fulfilling Formal Specifications ASAP by Model-free Reinforcement Learning
Mengyu Liu, Pengyuan Lu, Xin Chen +3
We propose a model-free reinforcement learning solution, namely the ASAP-Phi framework, to encourage an agent to fulfill a formal specification ASAP. The framework leverages a piec…
Using Semantic Information for Defining and Detecting OOD Inputs
Ramneet Kaur, Xiayan Ji, Souradeep Dutta +5
As machine learning models continue to achieve impressive performance across different tasks, the importance of effective anomaly detection for such models has increased as well. I…
DC4L: Distribution Shift Recovery via Data-Driven Control for Deep Learning Models
Vivian Lin, Kuk Jin Jang, Souradeep Dutta +3
Deep neural networks have repeatedly been shown to be non-robust to the uncertainties of the real world, even to naturally occurring ones. A vast majority of current approaches hav…
Credal Bayesian Deep Learning
Michele Caprio, Souradeep Dutta, Kuk Jin Jang +4
Uncertainty quantification and robustness to distribution shifts are important goals in machine learning and artificial intelligence. Although Bayesian Neural Networks (BNNs) allow…
Conservative Safety Monitors of Stochastic Dynamical Systems
Matthew Cleaveland, Oleg Sokolsky, Insup Lee +1
Generating accurate runtime safety estimates for autonomous systems is vital to ensuring their continued proliferation. However, exhaustive reasoning about future behaviors is gene…