most citedLearning Stabilizing Policies in Stochastic Control Systems

3 citations · 5 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG20221 cited

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…

cs.LG20221 cited

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…

cs.LG20223 cited

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…

cs.PL20221 cited

Differential Cost Analysis with Simultaneous Potentials and Anti-potentials

Đorđe Žikelić, Bor-Yuh Evan Chang, Pauline Bolignano +1

We present a novel approach to differential cost analysis that, given a program revision, attempts to statically bound the difference in resource usage, or cost, between the two pr…

cs.LG20211 cited

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…