Search Results for author: Daniel Palomar

Found 3 papers, 1 papers with code

Graphical Models in Heavy-Tailed Markets

no code implementations NeurIPS 2021 Jose Vinicius de Miranda Cardoso, Jiaxi Ying, Daniel Palomar

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.

Graph Learning

Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model

no code implementations NeurIPS 2020 Jiaxi Ying, José Vinícius de Miranda Cardoso , Daniel Palomar

In this paper, we consider the problem of learning a sparse graph from the Laplacian constrained Gaussian graphical model.

Graph Learning

A Unified Framework for Structured Graph Learning via Spectral Constraints

2 code implementations22 Apr 2019 Sandeep Kumar, Jiaxi Ying, José Vinícius de M. Cardoso, Daniel Palomar

Then we develop an optimization framework that leverages graph learning with specific structures via spectral constraints on graph matrices.

Graph Learning

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