1 citations · 1 across the 5 of their papers we have counts for
6 papers
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…
NeuroMambaLLM: Dynamic Graph Learning of fMRI Functional Connectivity in Autistic Brains Using Mamba and Language Model Reasoning
Yasaman Torabi, Parsa Razmara, Hamed Ajorlou +1
Large Language Models (LLMs) have demonstrated strong semantic reasoning across multimodal domains. However, their integration with graph-based models of brain connectivity remains…
Dirichlet Meets Horvitz and Thompson: Estimating Homophily in Large Networks via Sampling
Hamed Ajorlou, Gonzalo Mateos, Luana Ruiz
Assessing homophily in large-scale networks is central to understanding structural regularities in graphs, and thus inform the choice of models (such as graph neural networks) adop…
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…
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…
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…