23 citations · 48 across the 26 of their papers we have counts for
8 papers · 1 filter
Structure-Guided Input Graph for GNNs facing Heterophily
Victor M. Tenorio, Madeline Navarro, Samuel Rey +2
Graph Neural Networks (GNNs) have emerged as a promising tool to handle data exhibiting an irregular structure. However, most GNN architectures perform well on homophilic datasets,…
ML-SPEAK: A Theory-Guided Machine Learning Method for Studying and Predicting Conversational Turn-taking Patterns
Lisa R. O'Bryan, Madeline Navarro, Juan Segundo Hevia +1
Predicting team dynamics from personality traits remains a fundamental challenge for the psychological sciences and team-based organizations. Understanding how team composition gen…
Low-Rank Tensors for Multi-Dimensional Markov Models
Madeline Navarro, Sergio Rozada, Antonio G. Marques +1
This work presents a low-rank tensor model for multi-dimensional Markov chains. A common approach to simplify the dynamical behavior of a Markov chain is to impose low-rankness on…
Online Network Inference from Graph-Stationary Signals with Hidden Nodes
Andrei Buciulea, Madeline Navarro, Samuel Rey +2
Graph learning is the fundamental task of estimating unknown graph connectivity from available data. Typical approaches assume that not only is all information available simultaneo…
Redesigning graph filter-based GNNs to relax the homophily assumption
Samuel Rey, Madeline Navarro, Victor M. Tenorio +2
Graph neural networks (GNNs) have become a workhorse approach for learning from data defined over irregular domains, typically by implicitly assuming that the data structure is rep…
Fair CoVariance Neural Networks
Andrea Cavallo, Madeline Navarro, Santiago Segarra +1
Covariance-based data processing is widespread across signal processing and machine learning applications due to its ability to model data interconnectivities and dependencies. How…
Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior
Madeline Navarro, Samuel Rey, Andrei Buciulea +2
We propose estimating Gaussian graphical models (GGMs) that are fair with respect to sensitive nodal attributes. Many real-world models exhibit unfair discriminatory behavior due t…
Mitigating Subpopulation Bias for Fair Network Topology Inference
Madeline Navarro, Samuel Rey, Andrei Buciulea +2
We consider fair network topology inference from nodal observations. Real-world networks often exhibit biased connections based on sensitive nodal attributes. Hence, different subp…