4 citations · 10 across the 14 of their papers we have counts for
10 papers · 1 filter
Self-aware Social Learning over Graphs
Konstantinos Ntemos, Virginia Bordignon, Stefan Vlaski +1
In this paper we study the problem of social learning under multiple true hypotheses and self-interested agents which exchange information over a graph. In this setup, each agent r…
Competing Adaptive Networks
Stefan Vlaski, Ali H. Sayed
Adaptive networks have the capability to pursue solutions of global stochastic optimization problems by relying only on local interactions within neighborhoods. The diffusion of in…
Gramian-Based Adaptive Combination Policies for Diffusion Learning over Networks
Y. Efe Erginbas, Stefan Vlaski, Ali H. Sayed
This paper presents an adaptive combination strategy for distributed learning over diffusion networks. Since learning relies on the collaborative processing of the stochastic infor…
Second-Order Guarantees in Centralized, Federated and Decentralized Nonconvex Optimization
Stefan Vlaski, Ali H. Sayed
Rapid advances in data collection and processing capabilities have allowed for the use of increasingly complex models that give rise to nonconvex optimization problems. These formu…
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…
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…