12 papers
Localized Anomaly Detection via Differentiable D-vine Copulas
Nicholas Andrea Pearson, Francesca Zanello, Davide Russo +2
Vine copulas provide a flexible framework for modeling complex multivariate distributions through a hierarchical decomposition into bivariate pair-copulas. Fitting a D-vine require…
Guiding Neuro-Symbolic Scenario Generation with Spatio-Temporal Logic
Lorenzo Bonin, Francesco Giacomarra, Luca Bortolussi +2
The rapid advancement of autonomous driving (AD) technologies has outpaced the development of robust safety evaluation methods. Conventional testing relies on exposing AD systems t…
Conformal Predictive Monitoring for Multi-Modal Scenarios
Francesca Cairoli, Luca Bortolussi, Jyotirmoy V. Deshmukh +2
We consider the problem of quantitative predictive monitoring (QPM) of stochastic systems, i.e., predicting at runtime the degree of satisfaction of a desired temporal logic proper…
CoCAI: Copula-based Conformal Anomaly Identification for Multivariate Time-Series
Nicholas A. Pearson, Francesca Zanello, Davide Russo +2
We propose a novel framework that harnesses the power of generative artificial intelligence and copula-based modeling to address two critical challenges in multivariate time-series…
Diffusion-based Time Series Forecasting for Sewerage Systems
Nicholas A. Pearson, Francesca Cairoli, Luca Bortolussi +2
We introduce a novel deep learning approach that harnesses the power of generative artificial intelligence to enhance the accuracy of contextual forecasting in sewerage systems. By…
Certified Guidance for Planning with Deep Generative Models
Francesco Giacomarra, Mehran Hosseini, Nicola Paoletti +1
Deep generative models, such as generative adversarial networks and diffusion models, have recently emerged as powerful tools for planning tasks and behavior synthesis in autonomou…
Learning-Based Approaches to Predictive Monitoring with Conformal Statistical Guarantees
Francesca Cairoli, Luca Bortolussi, Nicola Paoletti
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 s…
Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes
Francesca Cairoli, Nicola Paoletti, Luca Bortolussi
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. Due to its relevance for…
Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking
Luca Bortolussi, Francesca Cairoli, Ginevra Carbone +1
Parametric verification of linear temporal properties for stochastic models can be expressed as computing the satisfaction probability of a certain property as a function of the pa…
Neural Predictive Monitoring under Partial Observability
Francesca Cairoli, Luca Bortolussi, Nicola Paoletti
We consider the problem of predictive monitoring (PM), i.e., predicting at runtime future violations of a system from the current state. We work under the most realistic settings w…
Abstraction of Markov Population Dynamics via Generative Adversarial Nets
Francesca Cairoli, Ginevra Carbone, Luca Bortolussi
Markov Population Models are a widespread formalism used to model the dynamics of complex systems, with applications in Systems Biology and many other fields. The associated Markov…
Adversarial Learning of Robust and Safe Controllers for Cyber-Physical Systems
Luca Bortolussi, Francesca Cairoli, Ginevra Carbone +2
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. T…