81 citations · 264 across the 35 of their papers we have counts for
39 papers
Tackling heavy-tailed noise in distributed estimation: Asymptotic performance and tradeoffs
Dragana Bajovic, Dusan Jakovetic, Soummya Kar +1
We present an algorithm for distributed estimation of an unknown vector parameter in the presence of heavy-tailed observation and communicatio…
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…
Sharp High-Probability Rates for Nonlinear SGD under Heavy-Tailed Noise via Symmetrization
Aleksandar Armacki, Dragana Bajovic, Dusan Jakovetic +1
We study convergence in high-probability of SGD-type methods in non-convex optimization and the presence of heavy-tailed noise. To combat the heavy-tailed noise, a general black-bo…
Distributed gradient methods under heavy-tailed communication noise
Manojlo Vukovic, Dusan Jakovetic, Dragana Bajovic +1
We consider a standard distributed optimization problem in which networked nodes collaboratively minimize the sum of their locally known convex costs. For this setting, we address…
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…