7 papers
Non-negative Weighted DAG Structure Learning
Samuel Rey, Seyed Saman Saboksayr, Gonzalo Mateos
We address the problem of learning the topology of directed acyclic graphs (DAGs) from nodal observations, which adhere to a linear structural equation model. Recent advances frame…
CoLiDE: Concomitant Linear DAG Estimation
Seyed Saman Saboksayr, Gonzalo Mateos, Mariano Tepper
We deal with the combinatorial problem of learning directed acyclic graph (DAG) structure from observational data adhering to a linear structural equation model (SEM). Leveraging a…
Dual-based Online Learning of Dynamic Network Topologies
Seyed Saman Saboksayr, Gonzalo Mateos
We investigate online network topology identification from smooth nodal observations acquired in a streaming fashion. Different from non-adaptive batch solutions, our distinctive g…
Accelerated Graph Learning from Smooth Signals
Seyed Saman Saboksayr, Gonzalo Mateos
We consider network topology identification subject to a signal smoothness prior on the nodal observations. A fast dual-based proximal gradient algorithm is developed to efficientl…
Online Graph Learning under Smoothness Priors
Seyed Saman Saboksayr, Gonzalo Mateos, Mujdat Cetin
The growing success of graph signal processing (GSP) approaches relies heavily on prior identification of a graph over which network data admit certain regularity. However, adaptat…
Online Discriminative Graph Learning from Multi-Class Smooth Signals
Seyed Saman Saboksayr, Gonzalo Mateos, Mujdat Cetin
Graph signal processing (GSP) is a key tool for satisfying the growing demand for information processing over networks. However, the success of GSP in downstream learning and infer…