4 citations · 5 across the 4 of their papers we have counts for
10 papers
Distributed Bayesian Learning of Dynamic States
Mert Kayaalp, Virginia Bordignon, Stefan Vlaski +2
This work studies networked agents cooperating to track a dynamical state of nature under partial information. The proposed algorithm is a distributed Bayesian filtering algorithm…
Dencentralized learning in the presence of low-rank noise
Roula Nassif, Virginia Bordignon, Stefan Vlaski +1
Observations collected by agents in a network may be unreliable due to observation noise or interference. This paper proposes a distributed algorithm that allows each node to impro…
Self-aware Social Learning over Graphs
Konstantinos Ntemos, Virginia Bordignon, Stefan Vlaski +1
In this paper we study the problem of social learning under multiple true hypotheses and self-interested agents which exchange information over a graph. In this setup, each agent r…
Deception in Social Learning
Konstantinos Ntemos, Virginia Bordignon, Stefan Vlaski +1
A common assumption in the social learning literature is that agents exchange information in an unselfish manner. In this work, we consider the scenario where a subset of agents ai…
Social learning under inferential attacks
Konstantinos Ntemos, Virginia Bordignon, Stefan Vlaski +1
A common assumption in the social learning literature is that agents exchange information in an unselfish manner. In this work, we consider the scenario where a subset of agents ai…
Network Classifiers Based on Social Learning
Virginia Bordignon, Stefan Vlaski, Vincenzo Matta +1
This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers…