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Found 11 papers, 5 papers with code
Polynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signals
This paper introduces Polynomial Graphical Lasso (PGL), a new approach to learning graph structures from nodal signals.
Network Topology Inference with Sparsity and Laplacian Constraints
We tackle the network topology inference problem by utilizing Laplacian constrained Gaussian graphical models, which recast the task as estimating a precision matrix in the form of a graph Laplacian.
Adaptive Estimation of Graphical Models under Total Positivity
We consider the problem of estimating (diagonally dominant) M-matrices as precision matrices in Gaussian graphical models.
Efficient and Scalable Parametric High-Order Portfolios Design via the Skew-t Distribution
Initially, profit and risk were measured by the first two moments of the portfolio's return, a. k. a.
Graphical Models in Heavy-Tailed Markets
Heavy-tailed statistical distributions have long been considered a more realistic statistical model for the data generating process in financial markets in comparison to their Gaussian counterpart.
Algorithms for Learning Graphs in Financial Markets
In the past two decades, the field of applied finance has tremendously benefited from graph theory.
Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model
In this paper, we consider the problem of learning a sparse graph from the Laplacian constrained Gaussian graphical model.
Does the $\ell_1$-norm Learn a Sparse Graph under Laplacian Constrained Graphical Models?
We propose a numerical algorithm based on based on the alternating direction method of multipliers, and establish its theoretical sequence convergence.
Structured Graph Learning Via Laplacian Spectral Constraints
Then we introduce a unified graph learning framework, lying at the integration of the spectral properties of the Laplacian matrix with Gaussian graphical modeling that is capable of learning structures of a large class of graph families.
A Unified Framework for Structured Graph Learning via Spectral Constraints
Then we develop an optimization framework that leverages graph learning with specific structures via spectral constraints on graph matrices.
Hankel Matrix Nuclear Norm Regularized Tensor Completion for $N$-dimensional Exponential Signals
Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging.
