4 citations · 5 across the 11 of their papers we have counts for
19 papers
Doubly Adaptive Social Learning
Marco Carpentiero, Virginia Bordignon, Vincenzo Matta +1
In social learning, a network of agents assigns probability scores (beliefs) to some hypotheses of interest, which rule the generation of local streaming data observed by each agen…
Non-Asymptotic Performance of Social Machine Learning Under Limited Data
Ping Hu, Virginia Bordignon, Mert Kayaalp +1
This paper studies the probability of error associated with the social machine learning framework, which involves an independent training phase followed by a cooperative decision-m…
Memory-Aware Social Learning under Partial Information Sharing
Michele Cirillo, Virginia Bordignon, Vincenzo Matta +1
This work examines a social learning problem, where dispersed agents connected through a network topology interact locally to form their opinions (beliefs) as regards certain hypot…
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…
Optimal Aggregation Strategies for Social Learning over Graphs
Ping Hu, Virginia Bordignon, Stefan Vlaski +1
Adaptive social learning is a useful tool for studying distributed decision-making problems over graphs. This paper investigates the effect of combination policies on the performan…
Social Opinion Formation and Decision Making Under Communication Trends
Mert Kayaalp, Virginia Bordignon, Ali H. Sayed
This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief informa…
Learning from Heterogeneous Data Based on Social Interactions over Graphs
Virginia Bordignon, Stefan Vlaski, Vincenzo Matta +1
This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions and aris…
Hidden Markov Modeling over Graphs
Mert Kayaalp, Virginia Bordignon, Stefan Vlaski +1
This work proposes a multi-agent filtering algorithm over graphs for finite-state hidden Markov models (HMMs), which can be used for sequential state estimation or for tracking opi…
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…
Partial Information Sharing over Social Learning Networks
Virginia Bordignon, Vincenzo Matta, Ali H. Sayed
This work addresses the problem of sharing partial information within social learning strategies. In traditional social learning, agents solve a distributed multiple hypothesis tes…
Adaptive Social Learning
Virginia Bordignon, Vincenzo Matta, Ali H. Sayed
This work proposes a novel strategy for social learning by introducing the critical feature of adaptation. In social learning, several distributed agents update continually their b…
Adaptation in Online Social Learning
Virginia Bordignon, Vincenzo Matta, Ali H. Sayed
This work studies social learning under non-stationary conditions. Although designed for online inference, classic social learning algorithms perform poorly under drifting conditio…
Learning Graph Influence from Social Interactions
Vincenzo Matta, Virginia Bordignon, Augusto Santos +1
In social learning, agents form their opinions or beliefs about certain hypotheses by exchanging local information. This work considers the recent paradigm of weak graphs, where th…
Interplay between Topology and Social Learning over Weak Graphs
Vincenzo Matta, Virginia Bordignon, Augusto Santos +1
We consider a social learning problem, where a network of agents is interested in selecting one among a finite number of hypotheses. We focus on weakly-connected graphs where the n…
Social Learning with Partial Information Sharing
Virginia Bordignon, Vincenzo Matta, Ali H. Sayed
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 in…