6 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…