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Found 6 papers, 0 papers with code
Federated Learning with Differential Privacy
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.
Data-driven abstractions via adaptive refinements and a Kantorovich metric [extended version]
In order to learn the optimal structure, we define a Kantorovich-inspired metric between Markov chains, and we use it as a loss function.
Learning stability guarantees for constrained switching linear systems from noisy observations
We present a data-driven framework based on Lyapunov theory to provide stability guarantees for a family of hybrid systems.
Data-driven memory-dependent abstractions of dynamical systems
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.
Black-box stability analysis of hybrid systems with sample-based multiple Lyapunov functions
More precisely, our contribution is the following: we derive a probabilistic upper bound on the CJSR of an unknown CSLS from a finite number of observations.
Learning stability guarantees for data-driven constrained switching linear systems
By generalizing previous results on arbitrary switching linear systems, we show that, by sampling a finite number of observations, we are able to construct an approximate Lyapunov function for the underlying system.
