DeepAI AI Chat
Log In Sign Up

Single Index Latent Variable Models for Network Topology Inference

by   Jonathan Mei, et al.

A semi-parametric, non-linear regression model in the presence of latent variables is applied towards learning network graph structure. These latent variables can correspond to unmodeled phenomena or unmeasured agents in a complex system of interacting entities. This formulation jointly estimates non-linearities in the underlying data generation, the direct interactions between measured entities, and the indirect effects of unmeasured processes on the observed data. The learning is posed as regularized empirical risk minimization. Details of the algorithm for learning the model are outlined. Experiments demonstrate the performance of the learned model on real data.


page 1

page 2

page 3

page 4


SILVar: Single Index Latent Variable Models

A semi-parametric, non-linear regression model in the presence of latent...

Model Criticism in Latent Space

Model criticism is usually carried out by assessing if replicated data g...

Learning Deep Bayesian Latent Variable Regression Models that Generalize: When Non-identifiability is a Problem

Bayesian Neural Networks with Latent Variables (BNN+LV's) provide uncert...

Ising Models with Latent Conditional Gaussian Variables

Ising models describe the joint probability distribution of a vector of ...

The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models

We propose a probabilistic model to infer supervised latent variables in...

Joint graph learning from Gaussian observations in the presence of hidden nodes

Graph learning problems are typically approached by focusing on learning...

The Mismatch Principle: Statistical Learning Under Large Model Uncertainties

We study the learning capacity of empirical risk minimization with regar...