Valentina Shumovskaia
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View article: Detection of Malicious Agents in Social Learning
Detection of Malicious Agents in Social Learning Open
Non-Bayesian social learning is a framework for distributed hypothesis testing aimed at learning the true state of the environment. Traditionally, the agents are assumed to receive observations conditioned on the same true state, although …
View article: Social Learning in Community Structured Graphs
Social Learning in Community Structured Graphs Open
Traditional social learning frameworks consider environments with a homogeneous state, where each agent receives observations conditioned on that true state of nature. In this work, we relax this assumption and study the distributed hypoth…
View article: Discovering Influencers in Opinion Formation Over Social Graphs
Discovering Influencers in Opinion Formation Over Social Graphs Open
The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on pr…
View article: Discovering Influencers in Opinion Formation over Social Graphs
Discovering Influencers in Opinion Formation over Social Graphs Open
The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on pr…
View article: Explainability and Graph Learning from Social Interactions
Explainability and Graph Learning from Social Interactions Open
Social learning algorithms provide models for the formation of opinions over social networks resulting from local reasoning and peer-to-peer exchanges. Interactions occur over an underlying graph topology, which describes the flow of infor…
View article: Online Graph Learning from Social Interactions
Online Graph Learning from Social Interactions Open
Social learning algorithms provide models for the formation of opinions over social networks resulting from local reasoning and peer-to-peer exchanges. Interactions occur over an underlying graph topology, which describes the flow of infor…
View article: Explainability and Graph Learning From Social Interactions
Explainability and Graph Learning From Social Interactions Open
Social learning algorithms provide models for the formation of opinions over social networks resulting from local reasoning and peer-to-peer exchanges. Interactions occur over an underlying graph topology, which describes the flow of infor…
View article: Online Graph Learning from Social Interactions
Online Graph Learning from Social Interactions Open
Social learning algorithms provide models for the formation of opinions over\nsocial networks resulting from local reasoning and peer-to-peer exchanges.\nInteractions occur over an underlying graph topology, which describes the flow\nof in…
View article: Linking bank clients using graph neural networks powered by rich transactional data
Linking bank clients using graph neural networks powered by rich transactional data Open
View article: EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data
EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data Open
In this paper, we discuss how modern deep learning approaches can be applied to the credit scoring of bank clients. We show that information about connections between clients based on money transfers between them allows us to significantly…
View article: EWS-GCN: Edge Weight-Shared Graph Convolutional Network for\n Transactional Banking Data
EWS-GCN: Edge Weight-Shared Graph Convolutional Network for\n Transactional Banking Data Open
In this paper, we discuss how modern deep learning approaches can be applied\nto the credit scoring of bank clients. We show that information about\nconnections between clients based on money transfers between them allows us to\nsignifican…
View article: Linking Bank Clients using Graph Neural Networks Powered by Rich\n Transactional Data
Linking Bank Clients using Graph Neural Networks Powered by Rich\n Transactional Data Open
Financial institutions obtain enormous amounts of data about user\ntransactions and money transfers, which can be considered as a large graph\ndynamically changing in time. In this work, we focus on the task of predicting\nnew interactions…