15 citations · 32 across the 11 of their papers we have counts for
14 papers · 1 filter
LDP for the covariance process in fully connected neural networks
Luisa Andreis, Federico Bassetti, Christian Hirsch
In this work, we study large deviation properties of the covariance process in fully connected Gaussian deep neural networks. More precisely, we establish a large deviation princip…
Convergence of cluster coagulation dynamics
Luisa Andreis, Tejas Iyer, Elena Magnanini
We study hydrodynamic limits of the cluster coagulation model; a coagulation model introduced by Norris [, 209(2):407-435 (2000)]. In this process, pair…
Spatial particle processes with coagulation: Gibbs-measure approach, gelation and Smoluchowski equation
Luisa Andreis, Wolfgang König, Heide Langhammer +1
We study a spatial Markovian particle system with pairwise coagulation, a spatial version of the Marcus--Lushnikov process: according to a coagulation kernel , particle pairs me…
An alternative approach to large deviations for the almost-critical Erdős-Rényi random graph
Luisa Andreis, Gianmarco Bet, Maxence Phalempin
We study the near-critical behavior of the sparse Erdős-Rényi random graph on vertices, where the connection probability satisfies $np = 1+θ(b_n^2/n)…
Gelation in cluster coagulation processes
Luisa Andreis, Tejas Iyer, Elena Magnanini
We consider the problem of gelation in the cluster coagulation model introduced by Norris [\textit{Comm. Math. Phys.}, 209(2):407-435 (2000)], where pairs of clusters of types $(x,…
Noise-induced periodicity in a frustrated network of interacting diffusions
Elisa Marini, Luisa Andreis, Francesca Collet +1
We investigate the emergence of a collective periodic behavior in a frustrated network of interacting diffusions. Particles are divided into two communities depending on their mutu…
A large-deviations principle for all the components in a sparse inhomogeneous random graph
Luisa Andreis, Wolfgang König, Heide Langhammer +1
We study an inhomogeneous sparse random graph on [N] = {1, . . . , N } as introduced in a seminal paper by Bollobas, Janson and Riordan (2007): vertices have a type (here in a comp…
Large deviations for Markov jump processes with uniformly diminishing rates
Andrea Agazzi, Luisa Andreis, Robert I. A. Patterson +1
We prove a large-deviation principle (LDP) for the sample paths of jump Markov processes in the small noise limit when, possibly, all the jump rates vanish uniformly, but slowly en…
A large-deviations principle for all the cluster sizes of a sparse Erdős-Rényi graph
Luisa Andreis, Wolfgang König, Robert I. A. Patterson
Let be the Erdős-Rényi graph with connection probability as for a fixed . We derive a large-deviatio…
Coexistence of stable limit cycles in a generalized Curie-Weiss model with dissipation
Luisa Andreis, Daniele Tovazzi
In this paper, we modify the Langevin dynamics associated to the generalized Curie-Weiss model by introducing noisy and dissipative evolution in the interaction potential. We show…
McKean-Vlasov limit for interacting systems with simultaneous jumps
Luisa Andreis, Paolo Dai Pra, Markus Fischer
Motivated by several applications, including neuronal models, we consider the McKean-Vlasov limit for mean-field systems of interacting diffusions with simultaneous jumps. We prove…
Ergodicity of a system of interacting random walks with asymmetric interaction
Luisa Andreis, Amine Asselah, Paolo Dai Pra
We study N interacting random walks on the positive integers. Each particle has drift δ towards infinity, a reflection at the origin, and a drift towards particles with lower posit…
On a class of Time-fractional Continuous-state Branching Processes
Luisa Andreis, Federico Polito, Laura Sacerdote
We propose a class of non-Markov population models with continuous or discrete state space via a limiting procedure involving sequences of rescaled and randomly time-changed Galton…
Strong existence and uniqueness of the stationary distribution for a stochastic inviscid dyadic model
Luisa Andreis, David Barbato, Francesca Collet +2
We consider an inviscid stochastically forced dyadic model, where the additive noise acts only on the first component. We prove that a strong solution for this problem exists and i…