Publications by Virginia Bordignon

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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 ...

This work examines a distributed learning problem where the agents of a network form their beliefs about certain hypotheses of interest. Each agent collects streaming (private) data and updates continually its belief by means of a diffusion strategy, which ...

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 ...

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 ...

In this paper we study the problem of social learning under multiple true hypotheses and self-interested agents that exchange information over a graph. In this setup, each agent receives data that might be generated from a different hypothesis (or state) t ...

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 ...

This paper studies the non-Asymptotic classification performance of the social machine learning strategy. This strategy involves an independent training phase followed by a cooperative inference phase to classify a growing number of samples. By considering ...

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 the network is partitioned into sending and receiving components, with the for ...

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 ...

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-making phase over a graph. This framework addresses the problem of classifyin ...

In social learning, a group of agents linked by a graph topology collect data and exchange opinions on some topic of interest, represented by a finite set of hypotheses. Traditional social learning algorithms allow all agents in the network to gain full co ...

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 locally to decide whether the observations they are receiving have ri ...

This work studies social learning under non-stationary conditions. Although designed for online inference, traditional social learning algorithms perform poorly under drifting conditions. To mitigate this drawback, we propose the Adaptive Social Learning ( ...

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 ...

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 ...

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 ...

Adaptive Social Learning (ASL) enables consistent truth learning in nonstationary environments. In this framework, agents linked by a graph, exchange their local beliefs with neighbors to track some underlying state of interest. This state can drift over t ...

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 hypotheses of interest. These opinions evolve over time, since the agents collect ...

Social learning algorithms provide a model for the formation and propagation of opinions over social networks. However, most studies focus on the case in which agents share their information synchronously over regular intervals. In this work, we analyze be ...

In social learning, a network of agents assigns probability scores (beliefs) to some hypotheses of interest, based on the observation of streaming data. First, each agent updates locally its belief with the information extracted from the current data throu ...

Virginia Bordignon

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Learning From Heterogeneous Data Based on Social Interactions Over Graphs

Interplay Between Topology and Social Learning Over Weak Graphs

Adaptive Social Learning

Network Classifiers Based On Social Learning

Self-Aware Social Learning Over Graphs

Partial Information Sharing Over Social Learning Networks

Performance of Social Machine Learning under Limited Data

Learning Graph Influence From Social Interactions

Social Learning with Disparate Hypotheses

Non-asymptotic performance of social machine learning under limited data

The Role of Memory in Social Learning When Sharing Partial Opinions

Social Learning With Partial Information Sharing

Adaptation in Online Social Learning

Optimal Combination Policies For Adaptive Social Learning

Social Opinion Formation and Decision Making Under Communication Trends

Social Learning Under Inferential Attacks

Adaptive Social Learning for Tracking Rare Transition Markov Chains

Memory-Aware Social Learning Under Partial Information Sharing

Asynchronous Social Learning

Social Learning with Adaptive Models