Interval Markov decision processes (IMDPs) provide a natural framework for modeling stochastic systems with uncertain transition probabilities, represented by probability intervals and resolved adversarially. Such uncertainty arises naturally, for example, when the transition model is learned from finite data or obtain...
Sarvin Bahmani, Soumyajit Paul, Sven Schewe et al.· 0 citations
Data-driven techniques have shown promising potential for checking behavior of complex systems operating in safety-critical domains against safety and other temporal requirements. This paper studies a class of data-driven techniques that are based on learning a representation of the system from data using non-parametri...
Discrete-event simulation is a standard technique for modelling and analysing the performance of computer systems, networks, and services. Although simulation tools are widely used, reasoning about the correctness and performance guarantees of the models they implement remains largely ad hoc: simulation outputs are int...
Moussa Labbadi, Rupak Majumdar, V. R. Sathiyanarayana et al.· 0 citations
A novel framework for analyzing metastability using Koopman theory is proposed, using a finite set of system trajectories to learn a representation of the dynamics that defines a latent space in which the system evolves linearly, thereby enabling a systematic characterization of metastable behavior through the spectral...
Rupak Majumdar, Mahmoud Salamati, Nikhil Singh et al.· 0 citations
A novel framework for computing rigorous bounds on the probability that a large language model (LLM) generates harmful output to a given prompt is proposed and a new application of the Clopper-Pearson confidence intervals is studied to obtain probably approximately correct bounds.
Mahdi Nazeri, Anne-Kathrin Schmuck, S. Soudjani et al.· arXiv.org· 0 citations
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