no code implementations • 20 Oct 2023 • Sebin Gracy, Ji Liu, Tamer Basar, Cesar A. Uribe
We identify a sufficient condition for exponential convergence to the disease-free equilibrium (DFE).
no code implementations • 25 Sep 2023 • Sebin Gracy, Brian D. O. Anderson, Mengbin Ye, Cesar A. Uribe
The paper deals with the spread of two competing viruses over a network of population nodes, accounting for pairwise interactions and higher-order interactions (HOI) within and between the population nodes.
no code implementations • 29 Mar 2023 • Sebin Gracy, Mengbin Ye, Brian D. O. Anderson, Cesar A. Uribe
We also identify sufficient conditions for the existence of a 2-coexistence (resp.
no code implementations • 23 Sep 2022 • Sebin Gracy, Mengbin Ye, Brian DO Anderson, Cesar A. Uribe
This paper studies the endemic behavior of a multi-competitive networked susceptible-infected-susceptible (SIS) model.
no code implementations • 28 Jun 2021 • Berkay Turan, Cesar A. Uribe, Hoi-To Wai, Mahnoosh Alizadeh
In this paper, we propose a first-order distributed optimization algorithm that is provably robust to Byzantine failures-arbitrary and potentially adversarial behavior, where all the participating agents are prone to failure.
no code implementations • 20 Nov 2020 • James Z. Hare, Cesar A. Uribe, Lance Kaplan, Ali Jadbabaie
Non-Bayesian social learning theory provides a framework that solves this problem in an efficient manner by allowing the agents to sequentially communicate and update their beliefs for each hypothesis over the network.
no code implementations • 9 Sep 2019 • James Z. Hare, Cesar A. Uribe, Lance Kaplan, Ali Jadbabaie
Non-Bayesian social learning theory provides a framework that models distributed inference for a group of agents interacting over a social network.
1 code implementation • 31 May 2018 • Hoi-To Wai, Wei Shi, Cesar A. Uribe, Angelia Nedich, Anna Scaglione
This paper studies an acceleration technique for incremental aggregated gradient ({\sf IAG}) method through the use of \emph{curvature} information for solving strongly convex finite sum optimization problems.