Search Results for author: Đorđe Žikelić

Found 6 papers, 3 papers with code

Learning Stabilizing Policies in Stochastic Control Systems

no code implementations24 May 2022 Đorđe Žikelić, Mathias Lechner, Krishnendu Chatterjee, Thomas A. Henzinger

In this work, we address the problem of learning provably stable neural network policies for stochastic control systems.

Stability Verification in Stochastic Control Systems via Neural Network Supermartingales

no code implementations17 Dec 2021 Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee, Thomas A. Henzinger

We consider the problem of formally verifying almost-sure (a. s.) asymptotic stability in discrete-time nonlinear stochastic control systems.

Infinite Time Horizon Safety of Bayesian Neural Networks

1 code implementation NeurIPS 2021 Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee, Thomas A. Henzinger

Bayesian neural networks (BNNs) place distributions over the weights of a neural network to model uncertainty in the data and the network's prediction.

reinforcement-learning Safe Exploration

Scalable Verification of Quantized Neural Networks (Technical Report)

1 code implementation15 Dec 2020 Thomas A. Henzinger, Mathias Lechner, Đorđe Žikelić

In this paper, we show that verifying the bit-exact implementation of quantized neural networks with bit-vector specifications is PSPACE-hard, even though verifying idealized real-valued networks and satisfiability of bit-vector specifications alone are each in NP.


Infinite-Duration All-Pay Bidding Games

no code implementations12 May 2020 Guy Avni, Ismaël Jecker, Đorđe Žikelić

In {\em bidding games}, however, the players have budgets, and in each turn, we hold an "auction" (bidding) to determine which player moves the token: both players simultaneously submit bids and the higher bidder moves the token.

Optimizing Expectation with Guarantees in POMDPs (Technical Report)

1 code implementation26 Nov 2016 Krishnendu Chatterjee, Petr Novotný, Guillermo A. Pérez, Jean-François Raskin, Đorđe Žikelić

In this work we go beyond both the "expectation" and "threshold" approaches and consider a "guaranteed payoff optimization (GPO)" problem for POMDPs, where we are given a threshold $t$ and the objective is to find a policy $\sigma$ such that a) each possible outcome of $\sigma$ yields a discounted-sum payoff of at least $t$, and b) the expected discounted-sum payoff of $\sigma$ is optimal (or near-optimal) among all policies satisfying a).

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