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We apply network Lasso to semi-supervised regression problems involving network-structured data. This approach lends quite naturally to highly scalable learning algorithms in the form of message passing over an empirical graph which represents the network structure of the data. By using a simple non-parametric regression model, which is motivated by a clustering hypothesis, we provide an analysis of the estimation error incurred by network Lasso. This analysis reveals conditions on the network structure and the available training data which guarantee network Lasso to be accurate. Remarkably, the accuracy of network Lasso is related to the existence of suciently large network flows over the empirical graph. Thus, our analysis reveals a connection between network Lasso and maximum network flow problems.
Original languageEnglish
Title of host publicationProceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019, Naha, Okinawa, Japan
Publication statusPublished - 2019
MoE publication typeA4 Conference publication
EventInternational Conference on Artificial Intelligence and Statistics - Naha, Japan
Duration: 16 Apr 201918 Apr 2019
Conference number: 22

Publication series

NameProceedings of Machine Learning Research
ISSN (Electronic)1938-7228


ConferenceInternational Conference on Artificial Intelligence and Statistics
Abbreviated titleAISTATS


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