1 citations · 1 across the 7 of their papers we have counts for
10 papers
Nonnegative DAG Learning via Concomitant Estimation
Madeline Navarro, Gonzalo Mateos, Samuel Rey
We study the problem of learning directed acyclic graphs (DAGs) with nonnegative edge weights from observational data. We propose the Nonnegative and Concomitant (NoCo) DAG estimat…
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 +1
This work addresses the problem of learning directed acyclic graphs (DAGs) from nodal observations adhering to a linear structural equation model. DAG structure learning is a centr…
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…
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…
Feature Selection via Graph Topology Inference for Soundscape Emotion Recognition
Samuel Rey, Luca Martino, Roberto San Millan +1
Research on soundscapes has shifted the focus of environmental acoustics from noise levels to the perception of sounds, incorporating contextual factors. Soundscape emotion recogni…
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…
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…