Person
Virginia Bordignon
Related publications (12)
Social Opinion Formation and Decision Making Under Communication Trends
Ali H. Sayed, Mert Kayaalp, Virginia Bordignon
This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...
Piscataway2024Partial Information Sharing Over Social Learning Networks
Ali H. Sayed, Virginia Bordignon
This work addresses the problem of sharing partial information within social learning strategies. In social learning, agents solve a distributed multiple hypothesis testing problem by performing two operations at each instant: first, agents incorporate inf ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023Learning From Heterogeneous Data Based on Social Interactions Over Graphs
Ali H. Sayed, Stefan Vlaski, Virginia Bordignon
This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions and arising from possibly different distributions. In the context of social learning ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023Hidden Markov Modeling Over Graphs
Ali H. Sayed, Mert Kayaalp, Stefan Vlaski, Virginia Bordignon
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 opinion formation over dynamic social networks. We show that the difference fro ...
IEEE2022Opinion Formation over Adaptive Networks
An adaptive network consists of multiple communicating agents, equipped with sensing and learning abilities that allow them to extract meaningful information from measurements. The objective of the network is to solve a global inference problem in a decent ...
EPFL2022Optimal Combination Policies For Adaptive Social Learning
Ping Hu, Stefan Vlaski, Virginia Bordignon
This paper investigates the effect of combination policies on the performance of adaptive social learning in non-stationary environments. By analyzing the relation between the error probability and the underlying graph topology, we prove that in the slow a ...
IEEE2022Social Learning with Disparate Hypotheses
Ali H. Sayed, Stefan Vlaski, Virginia Bordignon, Konstantinos Ntemos
In this paper we study the problem of social learning under multiple true hypotheses and self-interested agents. In this setup, each agent receives data that might be generated from a different hypothesis (or state) than the data other agents receive. In c ...
IEEE2022Adaptive Social Learning
Ali H. Sayed, Virginia Bordignon
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 belief about a phenomenon of interest through: i) direct observation of strea ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021Social Learning Under Inferential Attacks
Ali H. Sayed, Stefan Vlaski, Virginia Bordignon, Konstantinos Ntemos
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 aims at driving the network beliefs to the wrong hypothesis. The adversaries a ...
IEEE2021Network Classifiers Based On Social Learning
Ali H. Sayed, Stefan Vlaski, Virginia Bordignon
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 are aggregated. Combination over time means that the classifiers respond to ...
IEEE2021