63 citations · 323 across the 46 of their papers we have counts for
4 papers · 1 filter
Distributed Gaussian Learning over Time-varying Directed Graphs
Angelia Nedić, Alex Olshevsky, César A. Uribe
We present a distributed (non-Bayesian) learning algorithm for the problem of parameter estimation with Gaussian noise. The algorithm is expressed as explicit updates on the parame…
A Tutorial on Distributed (Non-Bayesian) Learning: Problem, Algorithms and Results
Angelia Nedić, Alex Olshevsky, César A. Uribe
We overview some results on distributed learning with focus on a family of recently proposed algorithms known as non-Bayesian social learning. We consider different approaches to t…
Geometrically Convergent Distributed Optimization with Uncoordinated Step-Sizes
Angelia Nedić, Alex Olshevsky, Wei Shi +1
A recent algorithmic family for distributed optimization, DIGing's, have been shown to have geometric convergence over time-varying undirected/directed graphs. Nevertheless, an ide…
Distributed Learning with Infinitely Many Hypotheses
Angelia Nedić, Alex Olshevsky, César Uribe
We consider a distributed learning setup where a network of agents sequentially access realizations of a set of random variables with unknown distributions. The network objective i…