81 citations · 264 across the 35 of their papers we have counts for
8 papers · 1 filter
Distributed Gradient Clustering: Convergence and the Effect of Initialization
Aleksandar Armacki, Himkant Sharma, Dragana Bajović +3
We study the effects of center initialization on the performance of a family of distributed gradient-based clustering algorithms introduced in [1], that work over connected network…
Large Deviation Upper Bounds and Improved MSE Rates of Nonlinear SGD: Heavy-tailed Noise and Power of Symmetry
Aleksandar Armacki, Shuhua Yu, Dragana Bajovic +2
We study large deviation upper bounds and mean-squared error (MSE) guarantees of a general framework of nonlinear stochastic gradient methods in the online setting, in the presence…
Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees
Aleksandar Armacki, Shuhua Yu, Pranay Sharma +4
We study high-probability convergence in online learning, in the presence of heavy-tailed noise. To combat the heavy tails, a general framework of nonlinear SGD methods is consider…
High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise
Aleksandar Armacki, Pranay Sharma, Gauri Joshi +3
We study high-probability convergence guarantees of learning on streaming data in the presence of heavy-tailed noise. In the proposed scenario, the model is updated in an online fa…
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…