98 citations · 291 across the 38 of their papers we have counts for
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Linear Speedup in Saddle-Point Escape for Decentralized Non-Convex Optimization
Stefan Vlaski, Ali H. Sayed
Under appropriate cooperation protocols and parameter choices, fully decentralized solutions for stochastic optimization have been shown to match the performance of centralized sol…
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 II: Polynomial Escape from Saddle-Points
Stefan Vlaski, Ali H. Sayed
The diffusion strategy for distributed learning from streaming data employs local stochastic gradient updates along with exchange of iterates over neighborhoods. In Part I [2] of t…
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…
Adaptation and learning over networks under subspace constraints -- Part II: Performance Analysis
Roula Nassif, Stefan Vlaski, Ali H. Sayed
Part I of this paper considered optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspac…