2 citations · 2 across the 2 of their papers we have counts for
5 papers
High-Probability Convergence in Decentralized Stochastic Optimization with Gradient Tracking
Aleksandar Armacki, Haoyuan Cai, Ali H. Sayed
We study high-probability (HP) convergence guarantees in decentralized stochastic optimization, where multiple agents collaborate to jointly train a model over a network. Existing…
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…
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…