We study the asymptotic learning rates of belief vectors in a distributed hypothesis testing
problem under linear and log-linear combination rules. We show that under both …

We study a social learning scheme where at every time instant, each agent chooses to
receive information from one of its neighbors at random. We show that under this sparser …

Traditional social learning frameworks consider environments with a homogeneous state,
where each agent receives observations conditioned on that true state of nature. In this …

The adaptive social learning paradigm helps model how networked agents are able to form
opinions on a state of nature and track its drifts in a changing environment. In this framework …

This work provides a comprehensive overview of adaptive diffusion networks, from the first
papers published on the subject to state-of-the-art solutions and current challenges. These …

We consider the problem of information aggregation in federated decision making, where a
group of agents collaborate to infer the underlying state of nature without sharing their …

This work studies the learning abilities of agents sharing partial beliefs over social networks.
The agents observe data that could have risen from one of several hypotheses and interact …

This paper investigates causal influences between agents linked by a social graph and
interacting over time. In particular, the work examines the dynamics of social learning …

This work proposes a decentralized architecture, where individual agents aim at solving a
classification problem while observing streaming features of different dimensions and …

Traditional social learning frameworks consider environments with a homogeneous state
where each agent receives observations conditioned on the same hypothesis. In this work …