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no code implementations • 24 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.

no code implementations • 17 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.

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.

1 code implementation • 15 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.

no code implementations • 12 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.

1 code implementation • 26 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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