no code implementations • 29 Mar 2023 • Spandan Das, Pavithra Prabhakar
In this paper, we consider the problem of probabilistic stability analysis of a subclass of Stochastic Hybrid Systems, namely, Polyhedral Probabilistic Hybrid Systems (PPHS), where the flow dynamics is given by a polyhedral inclusion, the discrete switching between modes happens probabilistically at the boundaries of their invariant regions and the continuous state is not reset during switching.
no code implementations • 6 Sep 2022 • Spandan Das, Pavithra Prabhakar
In this paper, we present a Bayesian method for statistical model checking (SMC) of probabilistic hyperproperties specified in the logic HyperPCTL* on discrete-time Markov chains (DTMCs).
no code implementations • 7 Oct 2021 • Pavithra Prabhakar
We present a notion of bisimulation that induces a reduced network which is semantically equivalent to the given neural network.
no code implementations • 27 Sep 2020 • Atreyee Kundu, Pavithra Prabhakar
We propose an automata theoretic learning algorithm for the identification of black-box switched linear systems whose switching logics are event-driven.
no code implementations • NeurIPS 2019 • Pavithra Prabhakar, Zahra Rahimi Afzal
The existing approaches reduce the output range analysis problem to satisfiability and optimization solving, which are NP-hard problems, and whose computational complexity increases with the number of neurons in the network.
