134 citations · 553 across the 48 of their papers we have counts for
7 papers · 1 filter
Nonlinear consensus+innovations under correlated heavy-tailed noises: Mean square convergence rate and asymptotics
Manojlo Vukovic, Dusan Jakovetic, Dragana Bajovic +1
We consider distributed recursive estimation of consensus+innovations type in the presence of heavy-tailed sensing and communication noises. We allow that the sensing and communica…
Large deviations rates for stochastic gradient descent with strongly convex functions
Dragana Bajovic, Dusan Jakovetic, Soummya Kar
Recent works have shown that high probability metrics with stochastic gradient descent (SGD) exhibit informativeness and in some cases advantage over the commonly adopted mean-squa…
A One-shot Framework for Distributed Clustered Learning in Heterogeneous Environments
Aleksandar Armacki, Dragana Bajovic, Dusan Jakovetic +1
The paper proposes a family of communication efficient methods for distributed learning in heterogeneous environments in which users obtain data from one of different distribut…
A Hessian inversion-free exact second order method for distributed consensus optimization
Dusan Jakovetic, Natasa Krejic, Natasa Krklec Jerinkic
We consider a standard distributed consensus optimization problem where a set of agents connected over an undirected network minimize the sum of their individual local strongly con…
Nonlinear gradient mappings and stochastic optimization: A general framework with applications to heavy-tail noise
Dusan Jakovetic, Dragana Bajovic, Anit Kumar Sahu +3
We introduce a general framework for nonlinear stochastic gradient descent (SGD) for the scenarios when gradient noise exhibits heavy tails. The proposed framework subsumes several…
Personalized Federated Learning via Convex Clustering
Aleksandar Armacki, Dragana Bajovic, Dusan Jakovetic +1
We propose a parametric family of algorithms for personalized federated learning with locally convex user costs. The proposed framework is based on a generalization of convex clust…