4 citations · 11 across the 14 of their papers we have counts for
15 papers
Memory-Dependent Interval Markov Chain Abstractions of Stochastic Dynamics
Menno van Zutphen, Adrien Banse, Domagoj Herceg +2
Finite-state interval Markov-chain (IMC) abstractions provide sound verification and performance bounds for continuous-state stochastic systems by enclosing cell-to-cell transition…
Robust Optimal Control of Arbitrarily Switched Systems: A Path-Complete Framework
Léa Ninite, Adrien Banse, Guillaume O. Berger +1
This paper addresses the robust control of switched systems under arbitrary switching with performance guarantees. We propose a framework that jointly synthesizes a feedback policy…
A Path-Complete Approach for Optimal Control of Switched Systems
Léa Ninite, Adrien Banse, Guillaume O. Berger +1
We study the problem of estimating the value function of discrete-time switched systems under arbitrary switching. Unlike the switched LQR problem, where both inputs and mode seque…
Comparing Labeled Markov Chains: A Cantor-Kantorovich Approach
Adrien Banse, Alessandro Abate, Raphaël M. Jungers
Labeled Markov Chains (or LMCs for short) are useful mathematical objects to model complex probabilistic languages. A central challenge is to compare two LMCs, for example to asses…
Agile Temporal Discretization for Symbolic Optimal Control
Adrien Janssens, Adrien Banse, Julien Calbert +1
As control systems grow in complexity, abstraction-based methods have become essential for designing controllers with formal guarantees. However, a key limitation of these methods…
Memory-dependent abstractions of stochastic systems through the lens of transfer operators
Adrien Banse, Giannis Delimpaltadakis, Luca Laurenti +2
With the increasing ubiquity of safety-critical autonomous systems operating in uncertain environments, there is a need for mathematical methods for formal verification of stochast…
A Cantor-Kantorovich Metric Between Markov Decision Processes with Application to Transfer Learning
Adrien Banse, Venkatraman Renganathan, Raphaël M. Jungers
We extend the notion of Cantor-Kantorovich distance between Markov chains introduced by (Banse et al., 2023) in the context of Markov Decision Processes (MDPs). The proposed metric…
Data-driven memory-dependent abstractions of dynamical systems via a Cantor-Kantorovich metric
Adrien Banse, Licio Romao, Alessandro Abate +1
Abstractions of dynamical systems enable their verification and the design of feedback controllers using simpler, usually discrete, models. In this paper, we propose a data-driven…
Dionysos.jl: a Modular Platform for Smart Symbolic Control
Julien Calbert, Adrien Banse, Benoît Legat +1
We introduce Dionysos.jl, a modular package for solving optimal control problems for complex dynamical systems using state-of-the-art and experimental techniques from symbolic cont…
Federated Learning with Differential Privacy
Adrien Banse, Jan Kreischer, Xavier Oliva i Jürgens
Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving client's private data from being shared among different parties. Neverthe…
Data-driven abstractions via adaptive refinements and a Kantorovich metric [extended version]
Adrien Banse, Licio Romao, Alessandro Abate +1
We introduce an adaptive refinement procedure for smart, and scalable abstraction of dynamical systems. Our technique relies on partitioning the state space depending on the observ…
Learning stability guarantees for constrained switching linear systems from noisy observations
Adrien Banse, Zheming Wang, Raphaël M. Jungers
We present a data-driven framework based on Lyapunov theory to provide stability guarantees for a family of hybrid systems. In particular, we are interested in the asymptotic stabi…
Data-driven memory-dependent abstractions of dynamical systems
Adrien Banse, Licio Romao, Alessandro Abate +1
We propose a sample-based, sequential method to abstract a (potentially black-box) dynamical system with a sequence of memory-dependent Markov chains of increasing size. We show th…
Black-box stability analysis of hybrid systems with sample-based multiple Lyapunov functions
Adrien Banse, Zheming Wang, Raphaël M. Jungers
We present a framework based on multiple Lyapunov functions to find probabilistic data-driven guarantees on the stability of unknown constrained switching linear systems (CSLS), wh…
Learning stability guarantees for data-driven constrained switching linear systems
Adrien Banse, Zheming Wang, Raphaël M. Jungers
We consider stability analysis of constrained switching linear systems in which the dynamics is unknown and whose switching signal is constrained by an automaton. We propose a data…