34 citations · 56 across the 7 of their papers we have counts for
7 papers
A Fundamental Limit in Decentralized Decision-Making
Marco Carpentiero, Felice Scala, Vincenzo Matta +1
In decentralized decision-making, several agents connected according to a network graph aim at solving a classification problem by collecting streaming observations. Due to decentr…
Performance Evaluation of Social Learning
Felice Scala, Marco Carpentiero, Vincenzo Matta +1
Social Learning is a decentralized decision-making paradigm in which spatially dispersed agents collect streaming observations regulated by one of a finite number of models (the hy…
Doubly Adaptive Social Learning
Marco Carpentiero, Virginia Bordignon, Vincenzo Matta +1
In social learning, a network of agents assigns probability scores (beliefs) to some hypotheses of interest, which rule the generation of local streaming data observed by each agen…
Differential error feedback for communication-efficient decentralized learning
Roula Nassif, Stefan Vlaski, Marco Carpentiero +2
Communication-constrained algorithms for decentralized learning and optimization rely on local updates coupled with the exchange of compressed signals. In this context, differentia…
Compressed Regression over Adaptive Networks
Marco Carpentiero, Vincenzo Matta, Ali H. Sayed
In this work we derive the performance achievable by a network of distributed agents that solve, adaptively and in the presence of communication constraints, a regression problem.…
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…
Distributed Adaptive Learning Under Communication Constraints
Marco Carpentiero, Vincenzo Matta, Ali H. Sayed
This work examines adaptive distributed learning strategies designed to operate under communication constraints. We consider a network of agents that must solve an online optimizat…