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 …

In this article, we studied the problem of cooperative inference where a group of agents
interacts over a network and seeks to estimate a joint parameter that best explains a set of …

We analyze a model of learning and belief formation in networks in which agents follow
Bayes rule yet they do not recall their history of past observations and cannot reason about …

We study the problem of cooperative inference where a group of agents interact over a
network and seek to estimate a joint parameter that best explains a set of observations …

We study the problem of non-Bayesian social learning with uncertain models, in which a
network of agents seek to cooperatively identify the state of the world based on a sequence …

In this article, we study the problem of cooperative inference, where a group of agents
interacts over a network and seeks to estimate a joint parameter that best explains a set of …

We propose a novel belief update algorithm for Distributed Non-Bayesian learning over time-
varying directed graphs, where a group of agents tries to collectively select a distribution that …

How can individuals exchange information to learn from each other despite their privacy
needs and security concerns? For example, consider individuals deliberating a contentious …

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 is …

A classic finding by (Kalish et al., 2007) shows that no language can be learned iteratively
by rational agents in a self-sustained manner. In other words, if A teaches a foreign …