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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…
On stochastic mirror-prox algorithms for stochastic Cartesian variational inequalities: randomized block coordinate and optimal averaging schemes
Farzad Yousefian, Angelia Nedich, Uday V. Shanbhag
Motivated by multi-user optimization problems and non-cooperative Nash games in uncertain regimes, we consider stochastic Cartesian variational inequalities (SCVI) where the set is…
Adaptive Sequential Stochastic Optimization
Craig Wilson, Venugopal Veeravalli, Angelia Nedich
A framework is introduced for sequentially solving convex stochastic minimization problems, where the objective functions change slowly, in the sense that the distance between succ…
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…
Epidemic Processes over Time-Varying Networks
Philip E. Paré, Angelia Nedić, Carolyn L. Beck
The spread of viruses in biological networks, computer networks, and human contact networks can have devastating effects; developing and analyzing mathematical models of these syst…