23 citations · 48 across the 26 of their papers we have counts for
29 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…
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…
Fair Feature Importance Scores via Feature Occlusion and Permutation
Camille Little, Madeline Navarro, Santiago Segarra +1
As machine learning models increasingly impact society, their opaque nature poses challenges to trust and accountability, particularly in fairness contexts. Understanding how indiv…
Stationarity and Spectral Characterization of Random Signals on Simplicial Complexes
Madeline Navarro, Andrei Buciulea, Santiago Segarra +1
It is increasingly common for data to possess intricate structure, necessitating new models and analytical tools. Graphs, a prominent type of structure, can encode the relationship…
Learning Time-Varying Turn-Taking Behavior in Group Conversations
Madeline Navarro, Lisa O'Bryan, Santiago Segarra
We propose a flexible probabilistic model for predicting turn-taking patterns in group conversations based solely on individual characteristics and past speaking behavior. Many mod…
Estimating Fair Graphs from Graph-Stationary Data
Madeline Navarro, Andrei Buciulea, Samuel Rey +2
We estimate fair graphs from graph-stationary nodal observations such that connections are not biased with respect to sensitive attributes. Edges in real-world graphs often exhibit…
Adaptive Node Feature Selection For Graph Neural Networks
Madeline Navarro, Ali Azizpour, Santiago Segarra
We propose an adaptive node feature selection approach for graph neural networks (GNNs) that identifies and removes unnecessary features during training. The ability to measure how…
Adaptive Graph Coarsening for Efficient GNN Training
Rostyslav Olshevskyi, Madeline Navarro, Santiago Segarra
We propose an adaptive graph coarsening method to jointly learn graph neural network (GNN) parameters and merge nodes via K-means clustering during training. As real-world graphs g…
Adapting to Heterophilic Graph Data with Structure-Guided Neighbor Discovery
Victor M. Tenorio, Madeline Navarro, Samuel Rey +2
Graph Neural Networks (GNNs) often struggle with heterophilic data, where connected nodes may have dissimilar labels, as they typically assume homophily and rely on local message p…
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…
Recovering Missing Node Features with Local Structure-based Embeddings
Victor M. Tenorio, Madeline Navarro, Santiago Segarra +1
Node features bolster graph-based learning when exploited jointly with network structure. However, a lack of nodal attributes is prevalent in graph data. We present a framework to…
SC-MAD: Mixtures of Higher-order Networks for Data Augmentation
Madeline Navarro, Santiago Segarra
The myriad complex systems with multiway interactions motivate the extension of graph-based pairwise connections to higher-order relations. In particular, the simplicial complex ha…
Data Augmentation via Subgroup Mixup for Improving Fairness
Madeline Navarro, Camille Little, Genevera I. Allen +1
In this work, we propose data augmentation via pairwise mixup across subgroups to improve group fairness. Many real-world applications of machine learning systems exhibit biases ac…
Joint Network Topology Inference in the Presence of Hidden Nodes
Madeline Navarro, Samuel Rey, Andrei Buciulea +2
We investigate the increasingly prominent task of jointly inferring multiple networks from nodal observations. While most joint inference methods assume that observations are avail…
Joint graph learning from Gaussian observations in the presence of hidden nodes
Samuel Rey, Madeline Navarro, Andrei Buciulea +2
Graph learning problems are typically approached by focusing on learning the topology of a single graph when signals from all nodes are available. However, many contemporary setups…
GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex Clustering
Madeline Navarro, Santiago Segarra
We develop a novel data-driven nonlinear mixup mechanism for graph data augmentation and present different mixup functions for sample pairs and their labels. Mixup is a data augmen…
Joint Network Topology Inference via a Shared Graphon Model
Madeline Navarro, Santiago Segarra
We consider the problem of estimating the topology of multiple networks from nodal observations, where these networks are assumed to be drawn from the same (unknown) random graph m…