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Virginia Bordignon

Non-asymptotic performance of social machine learning under limited data

Ali H. Sayed, Virginia Bordignon, Mert Kayaalp, Ping Hu

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 ...

2025

Adaptive Social Learning for Tracking Rare Transition Markov Chains

Ali H. Sayed, Virginia Bordignon, Malek Khammassi

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 ...

European Signal Processing Conference, EUSIPCO2024

Social Learning with Adaptive Models

Ali H. Sayed, Virginia Bordignon

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 ...

IEEE2024

Social Opinion Formation and Decision Making Under Communication Trends

Ali H. Sayed, Virginia Bordignon, Mert Kayaalp

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 ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Memory-Aware Social Learning Under Partial Information Sharing

Ali H. Sayed, Virginia Bordignon

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Partial 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 INC2023

The Role of Memory in Social Learning When Sharing Partial Opinions

Ali H. Sayed, Virginia Bordignon

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 ...

IEEE2023

Performance of Social Machine Learning under Limited Data

Ali H. Sayed, Virginia Bordignon, Mert Kayaalp, Ping Hu

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 ...

IEEE2023

Asynchronous Social Learning

Ali H. Sayed, Virginia Bordignon, Mert Kayaalp, Valentina Shumovskaia

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 ...

IEEE2023

Self-Aware Social Learning Over Graphs

Ali H. Sayed, Virginia Bordignon, Stefan Vlaski, Konstantinos Ntemos

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023