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Found 6 papers, 3 papers with code
Learning-Based Approaches to Predictive Monitoring with Conformal Statistical Guarantees
This tutorial focuses on efficient methods to predictive monitoring (PM), the problem of detecting at runtime future violations of a given requirement from the current state of a system.
Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes
We consider the problem of predictive monitoring (PM), i. e., predicting at runtime the satisfaction of a desired property from the current system's state.
Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking
As observations are costly and noisy, smMC is framed as a Bayesian inference problem so that the estimates have an additional quantification of the uncertainty.
Neural Predictive Monitoring under Partial Observability
We consider the problem of predictive monitoring (PM), i. e., predicting at runtime future violations of a system from the current state.
Abstraction of Markov Population Dynamics via Generative Adversarial Nets
Markov Population Models are a widespread formalism used to model the dynamics of complex systems, with applications in Systems Biology and many other fields.
Adversarial Learning of Robust and Safe Controllers for Cyber-Physical Systems
We introduce a novel learning-based approach to synthesize safe and robust controllers for autonomous Cyber-Physical Systems and, at the same time, to generate challenging tests.
