8 papers
On the Probabilistic Learnability of Compact Neural Network Preimage Bounds
Luca Marzari, Manuele Bicego, Ferdinando Cicalese +1
Although recent provable methods have been developed to compute preimage bounds for neural networks, their scalability is fundamentally limited by the #P-hardness of the problem. I…
Probabilistically Tightened Linear Relaxation-based Perturbation Analysis for Neural Network Verification
Luca Marzari, Ferdinando Cicalese, Alessandro Farinelli
We present robabilistically ightened near elaxation-based erturbation nalysis (), a nove…
Advancing Neural Network Verification through Hierarchical Safety Abstract Interpretation
Luca Marzari, Isabella Mastroeni, Alessandro Farinelli
Traditional methods for formal verification (FV) of deep neural networks (DNNs) are constrained by a binary encoding of safety properties, where a model is classified as either saf…
Learning Symbolic Persistent Macro-Actions for POMDP Solving Over Time
Celeste Veronese, Daniele Meli, Alessandro Farinelli
This paper proposes an integration of temporal logical reasoning and Partially Observable Markov Decision Processes (POMDPs) to achieve interpretable decision-making under uncertai…
Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation
Luca Marzari, Francesco Trotti, Enrico Marchesini +1
Achieving safe autonomous navigation systems is critical for deploying robots in dynamic and uncertain real-world environments. In this paper, we propose a hierarchical control fra…
Depth-Constrained ASV Navigation with Deep RL and Limited Sensing
Amirhossein Zhalehmehrabi, Daniele Meli, Francesco Dal Santo +2
Autonomous Surface Vehicles (ASVs) play a crucial role in maritime operations, yet their navigation in shallow-water environments remains challenging due to dynamic disturbances an…