7 papers
Directed Graph Topology Inference via Graph Filter Identification
Rasoul Shafipour, Andrei Buciulea, Santiago Segarra +2
We address the problem of inferring a directed network from nodal measurements generated by linear diffusion dynamics on the sought graph. Observations are modeled as the outputs o…
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…
Concomitant DAG Learning: On the Roles of Noise Adaptivity, Sparsity, and Non-negativity
Gonzalo Mateos, Samuel Rey, Hamed Ajorlou +1
Directed acyclic graphs (DAGs) constitute a central modeling tool to enable principled reasoning about cause-effect interactions in complex systems. However, since the causal struc…
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…
Directed Acyclic Graph Convolutional Networks
Samuel Rey, Hamed Ajorlou, Gonzalo Mateos
Directed acyclic graphs (DAGs) are central to science and engineering applications including causal inference, scheduling, and neural architecture search. In this work, we introduc…
Non-negative DAG Learning from Time-Series Data
Samuel Rey, Gonzalo Mateos
This work aims to learn the directed acyclic graph (DAG) that captures the instantaneous dependencies underlying a multivariate time series. The observed data follow a linear struc…