5 papers · 1 filter
BUILD with Precision: Bottom-Up Inference of Linear DAGs
Hamed Ajorlou, Samuel Rey, Gonzalo Mateos +2
Learning the structure of directed acyclic graphs (DAGs) from observational data is a central problem in causal discovery, statistical signal processing, and machine learning. Unde…
Exploiting Non-Negativity in DAG Structure Learning
Samuel Rey, Madeline navarro, Gonzalo Mateos
This work addresses the problem of learning directed acyclic graphs (DAGs) from nodal observations generated by a linear structural equation model. DAG learning is a central task i…
Online Learning Of Expanding Graphs
Samuel Rey, Bishwadeep Das, Elvin Isufi
This paper addresses the problem of online network topology inference for expanding graphs from a stream of spatiotemporal signals. Online algorithms for dynamic graph learning are…
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…
Convolutional Learning on Directed Acyclic Graphs
Samuel Rey, Hamed Ajorlou, Gonzalo Mateos
We develop a novel convolutional architecture tailored for learning from data defined over directed acyclic graphs (DAGs). DAGs can be used to model causal relationships among vari…