5 citations · 11 across the 10 of their papers we have counts for
3 papers · 1 filter
Efficient learning of differential network in multi-source non-paranormal graphical models
Mojtaba Nikahd, Seyed Abolfazl Motahari
This paper addresses learning of sparse structural changes or differential network between two classes of non-paranormal graphical models. We assume a multi-source and heterogeneou…
Structure Learning of Sparse GGMs over Multiple Access Networks
Mostafa Tavassolipour, Armin Karamzade, Reza Mirzaeifard +2
A central machine is interested in estimating the underlying structure of a sparse Gaussian Graphical Model (GGM) from datasets distributed across multiple local machines. The loca…
Learning of Tree-Structured Gaussian Graphical Models on Distributed Data under Communication Constraints
Mostafa Tavassolipour, Seyed Abolfazl Motahari, Mohammad-Taghi Manzuri Shalmani
In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where eac…