3 citations · 6 across the 7 of their papers we have counts for
8 papers
Finite-sample guarantees for data-driven operator splitting methods via martingale inequalities
Andrea Martin, Filippo Fabiani, Giuseppe Belgioioso
Operator splitting methods are a fundamental class of algorithms for solving structured monotone inclusion problems arising in optimization, control, and game theory. We consider t…
Generalization as a robust performance property of learning-enabled dynamical systems
Filippo Fabiani
By focusing on algorithmic stability as a means of establishing out-of-sample bounds, we provide a system-theoretic interpretation of generalization in learning-enabled dynamical s…
Finite-sample guarantees for data-driven forward-backward operator methods
Filippo Fabiani, Barbara Franci
We establish finite sample certificates on the quality of solutions produced by data-based forward-backward (FB) operator splitting schemes. As frequently happens in stochastic reg…
Concentration inequalities for semidefinite least squares based on data
Filippo Fabiani, Andrea Simonetto
We study data-driven least squares (LS) problems with semidefinite (SD) constraints and derive finite-sample guarantees on the spectrum of their optimal solutions when these constr…
Distributed equilibrium seeking in aggregative games: linear convergence under singular perturbations lens
Guido Carnevale, Filippo Fabiani, Filiberto Fele +2
We present a fully-distributed algorithm for Nash equilibrium seeking in aggregative games over networks. The proposed scheme endows each agent with a gradient-based scheme equippe…
Actively learning equilibria in Nash games with misleading information
Barbara Franci, Filippo Fabiani, Alberto Bemporad
We develop a scheme based on active learning to compute equilibria in a generalized Nash equilibrium problem (GNEP). Specifically, an external observer (or entity), with little kno…
A Gauss-Seidel method for solving multi-leader-multi-follower games
Barbara Franci, Filippo Fabiani, Martin Schmidt +1
We design a computational approach to find equilibria in a class of Nash games possessing a hierarchical structure. By using tools from mixed-integer optimization and the character…
Best-response algorithms for a class of monotone Nash equilibrium problems with mixed-integer variables
Filippo Fabiani, Simone Sagratella
We characterize the convergence properties of traditional best-response (BR) algorithms in computing solutions to mixed-integer Nash equilibrium problems (MI-NEPs) that turn into a…