We consider the problem of distributed learning, where a network of agents collectively aim
to agree on a hypothesis that best explains a set of distributed observations of conditionally …

This paper considers a problem of distributed hypothesis testing over a network. Individual
nodes in a network receive noisy local (private) observations whose distribution is …

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 the …

This paper addresses the problem of distributed detection in multi-agent networks. Agents
receive private signals about an unknown state of the world. The underlying state is globally …

We study the problem of cooperative learning with a network of agents where some agents
repeatedly access information about a random variable with unknown distribution. The …

We study a setting where a group of agents, each receiving partially informative private
signals, seek to collaboratively learn the true underlying state of the world (from a finite set of …

Autonomous robot networks are an effective tool for monitoring large-scale environmental
fields. This paper proposes distributed control strategies for localizing the source of a noisy …

In this paper we present an optimization-based view of distributed parameter estimation and
observational social learning in networks. Agents receive a sequence of random …

This paper addresses the problem of non-Bayesian learning over multi-agent networks,
where agents repeatedly collect partially informative observations about an unknown state …

This paper addresses the problem of collaborative estimation and tracking of dynamic
phenomena via a wireless sensor network. A distributed linear estimator (ie, a type of a …