98 citations · 291 across the 38 of their papers we have counts for
8 papers · 1 filter
Graph-Aware Learning Rates for Decentralized Optimization
Aaron Fainman, Stefan Vlaski
We propose an adaptive step-size rule for decentralized optimization. Choosing a step-size that balances convergence and stability is challenging. This is amplified in the decentra…
On the Convergence of Decentralized Stochastic Gradient-Tracking with Finite-Time Consensus
Aaron Fainman, Stefan Vlaski
Algorithms for decentralized optimization and learning rely on local optimization steps coupled with combination steps over a graph. Recent works have demonstrated that using a tim…
Learned Finite-Time Consensus for Distributed Optimization
Aaron Fainman, Stefan Vlaski
Most algorithms for decentralized learning employ a consensus or diffusion mechanism to drive agents to a common solution of a global optimization problem. Generally this takes the…
Quantization for decentralized learning under subspace constraints
Roula Nassif, Stefan Vlaski, Marco Carpentiero +3
In this paper, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers a…
Tracking Performance of Online Stochastic Learners
Stefan Vlaski, Elsa Rizk, Ali H. Sayed
The utilization of online stochastic algorithms is popular in large-scale learning settings due to their ability to compute updates on the fly, without the need to store and proces…
Regularized Diffusion Adaptation via Conjugate Smoothing
Stefan Vlaski, Lieven Vandenberghe, Ali H. Sayed
The purpose of this work is to develop and study a distributed strategy for Pareto optimization of an aggregate cost consisting of regularized risks. Each risk is modeled as the ex…
Second-Order Guarantees of Stochastic Gradient Descent in Non-Convex Optimization
Stefan Vlaski, Ali H. Sayed
Recent years have seen increased interest in performance guarantees of gradient descent algorithms for non-convex optimization. A number of works have uncovered that gradient noise…
Distributed Learning in Non-Convex Environments -- Part I: Agreement at a Linear Rate
Stefan Vlaski, Ali H. Sayed
Driven by the need to solve increasingly complex optimization problems in signal processing and machine learning, there has been increasing interest in understanding the behavior o…