2 citations · 2 across the 5 of their papers we have counts for
6 papers
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…
DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
Shae McFadden, Myles Foley, Mario D'Onghia +4
Malware detection in real-world settings must deal with evolving threats, limited labeling budgets, and uncertain predictions. Traditional classifiers, without additional mechanism…
LTL Verification of Memoryful Neural Agents
Mehran Hosseini, Alessio Lomuscio, Nicola Paoletti
We present a framework for verifying Memoryful Neural Multi-Agent Systems (MN-MAS) against full Linear Temporal Logic (LTL) specifications. In MN-MAS, agents interact with a non-de…
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…
Probabilistic Reach-Avoid for Bayesian Neural Networks
Matthew Wicker, Luca Laurenti, Andrea Patane +3
Model-based reinforcement learning seeks to simultaneously learn the dynamics of an unknown stochastic environment and synthesise an optimal policy for acting in it. Ensuring the s…