7 papers
Probabilistic Verification of Recurrent Neural Networks for Single and Multi-Agent Reinforcement Learning
Luca Marzari, Enrico Marchesini
History-dependent policies induced by recurrent neural networks (RNNs) rely on latent hidden state dynamics, making verification in partially observable reinforcement learning (RL)…
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…
ModelVerification.jl: a Comprehensive Toolbox for Formally Verifying Deep Neural Networks
Tianhao Wei, Hanjiang Hu, Luca Marzari +4
Deep Neural Networks (DNN) are crucial in approximating nonlinear functions across diverse applications, ranging from image classification to control. Verifying specific input-outp…
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…
Improving Policy Optimization via -Retrain
Luca Marzari, Priya L. Donti, Changliu Liu +1
We present -retrain, an exploration strategy encouraging a behavioral preference while optimizing policies with monotonic improvement guarantees. To this end, we intro…
RobustX: Robust Counterfactual Explanations Made Easy
Junqi Jiang, Luca Marzari, Aaryan Purohit +1
The increasing use of Machine Learning (ML) models to aid decision-making in high-stakes industries demands explainability to facilitate trust. Counterfactual Explanations (CEs) ar…