4 citations · 17 across the 19 of their papers we have counts for
8 papers · 1 filter
Neural Control and Certificate Repair via Runtime Monitoring
Emily Yu, Đorđe Žikelić, Thomas A. Henzinger
Learning-based methods provide a promising approach to solving highly non-linear control tasks that are often challenging for classical control methods. To ensure the satisfaction…
Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees
Đorđe Žikelić, Mathias Lechner, Abhinav Verma +2
Reinforcement learning has shown promising results in learning neural network policies for complicated control tasks. However, the lack of formal guarantees about the behavior of s…
Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks
Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee +2
We study the problem of training and certifying adversarially robust quantized neural networks (QNNs). Quantization is a technique for making neural networks more efficient by runn…
Learning Control Policies for Stochastic Systems with Reach-avoid Guarantees
Đorđe Žikelić, Mathias Lechner, Thomas A. Henzinger +1
We study the problem of learning controllers for discrete-time non-linear stochastic dynamical systems with formal reach-avoid guarantees. This work presents the first method for p…
Learning Provably Stabilizing Neural Controllers for Discrete-Time Stochastic Systems
Matin Ansaripour, Krishnendu Chatterjee, Thomas A. Henzinger +2
We consider the problem of learning control policies in discrete-time stochastic systems which guarantee that the system stabilizes within some specified stabilization region with…
Learning Stabilizing Policies in Stochastic Control Systems
Đorđe Žikelić, Mathias Lechner, Krishnendu Chatterjee +1
In this work, we address the problem of learning provably stable neural network policies for stochastic control systems. While recent work has demonstrated the feasibility of certi…
Stability Verification in Stochastic Control Systems via Neural Network Supermartingales
Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee +1
We consider the problem of formally verifying almost-sure (a.s.) asymptotic stability in discrete-time nonlinear stochastic control systems. While verifying stability in determinis…
Infinite Time Horizon Safety of Bayesian Neural Networks
Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee +1
Bayesian neural networks (BNNs) place distributions over the weights of a neural network to model uncertainty in the data and the network's prediction. We consider the problem of v…