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Madeline Navarro
If the following is not up to date, feel free to check my Google Scholar and arXiv profiles.
Preprints
Estimating Fair Graphs from Graph-Stationary Data [arXiv]
Madeline Navarro,
Andrei Buciulea,
Samuel Rey,
Antonio G. Marques,
and
Santiago Segarra,
preprint
Learning Time-Varying Turn-Taking Behavior in Group Conversations [arXiv]
Madeline Navarro,
Lisa O'Bryan,
and
Santiago Segarra,
preprint
Adaptive Node Feature Selection for Graph Neural Networks [arXiv]
Ali Azizpour,
Madeline Navarro,
and
Santiago Segarra,
preprint
Adaptive Graph Coarsening for Efficient GNN Training [arXiv]
Rostyslav Olshevskyi,
Madeline Navarro,
and
Santiago Segarra,
preprint
Adapting to Heterophilic Graph Data with Structure-Guided Neighbor Discovery [arXiv]
Victor M. Tenorio,
Madeline Navarro,
Samuel Rey,
Santiago Segarra,
and
Antonio G. Marques,
preprint
Fair Feature Importance Scores via Feature Occlusion and Permutation [OpenReview]
Camille Little,
Madeline Navarro,
Santiago Segarra,
and
Genevera I. Allen,
ICLR 2025 Workshop Advances in Financial AI
Network Clustering for Latent Space and Changepoint Detection [arXiv]
Madeline Navarro,
Genevera I. Allen,
and
Michael Weylandt,
preprint
Journal publications
ML-SPEAK: A theory-guided machine learning method for studying and predicting conversational turn-taking patterns [arXiv]
Lisa O'Bryan,
Madeline Navarro,
Juan Segundo Hevia,
and
Santiago Segarra,
Journal of Personality and Social Psychology,
2025
Joint Network Topology Inference in the Presence of Hidden Nodes [arXiv]
Madeline Navarro,
Samuel Rey,
Andrei Buciulea,
Antonio G. Marques,
and
Santiago Segarra,
IEEE Transactions on Signal Processing,
2024
Joint Network Topology Inference via a Shared Graphon Model [arXiv]
Madeline Navarro
and
Santiago Segarra,
IEEE Transactions on Signal Processing,
2022
Joint Inference of Multiple Graphs from Matrix Polynomials [OpenReview]
Madeline Navarro,
Yuhao Wang,
Antonio G. Marques,
Caroline Uhler,
and
Santiago Segarra,
Journal of Machine Learning Research,
2022
Conference papers
Bayesian Filtering on Graphs
Bishwadeep Das,
Madeline Navarro,
Santiago Segarra,
and
Elvin Isufi,
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
2025
ICASSP 2025 Best Paper Award
Low-Rank Tensors for Multi-Dimensional Markov Models [arXiv]
Madeline Navarro,
Sergio Rozada,
Antonio G. Marques,
and
Santiago Segarra,
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
2025
Online Network Inference from Graph-Stationary Signals with Hidden Nodes [arXiv]
Andrei Buciulea,
Madeline Navarro,
Samuel Rey,
Santiago Segarra,
and
Antonio G. Marques,
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
2025
Fair coVariance Neural Networks [arXiv]
Andrea Cavallo,
Madeline Navarro,
Santiago Segarra,
and
Elvin Isufi,
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
2025
Redesigning Graph Filter-Based GNNs to Relax the Homophily Assumption [arXiv]
Samuel Rey,
Madeline Navarro,
Victor M. Tenorio,
Santiago Segarra,
and
Antonio G. Marques,
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
2025
Structure-Guided Input Graph for GNNs Facing Heterophily [arXiv]
Victor M. Tenorio,
Madeline Navarro,
Samuel Rey,
Santiago Segarra,
and
Antonio G. Marques,
IEEE Asilomar Conference on Signals, Systems, and Computers,
2024
Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior [OpenReview]
Madeline Navarro,
Samuel Rey,
Andrei Buciulea,
Antonio G. Marques,
and
Santiago Segarra,
Advances in Neural Information Processing Systems (NeurIPS),
2024
Mitigating Subpopulation Bias for Fair Network Topology Inference [arXiv]
Madeline Navarro,
Samuel Rey,
Andrei Buciulea,
Antonio G. Marques,
and
Santiago Segarra,
IEEE European Signal Processing (EUSIPCO),
2024
SC-MAD: Mixtures of Higher-Order Networks for Data Augmentation [arXiv]
Madeline Navarro
and
Santiago Segarra,
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
2024
Data Augmentation via Subgroup Mixup for Improving Fairness [arXiv]
Madeline Navarro,
Camille Little,
Genevera I. Allen,
and
Santiago Segarra,
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
2024
Recovering Missing Node Features with Local Structure-Based Embeddings [arXiv]
Victor M. Tenorio,
Madeline Navarro,
Santiago Segarra,
and
Antonio G. Marques,
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
2024
GraphMAD: Graph mixup for data augmentation using data-driven convex clustering [arXiv]
Madeline Navarro
and
Santiago Segarra,
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
2023
Joint Graph Learning from Gaussian Observations in the Presence of Hidden Nodes [arXiv]
Samuel Rey,
Madeline Navarro,
Andrei Buciulea,
Santiago Segarra,
and
Antonio G. Marques,
IEEE Asilomar Conference on Signals, Systems, and Computers,
2022
Graphon-Aided Joint Estimation of Multiple Graphs [arXiv]
Madeline Navarro
and
Santiago Segarra,
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
2022
Joint Inference of Multiple Graphs with Hidden Variables from Stationary Graph Signals [arXiv]
Samuel Rey,
Andrei Buciulea,
Madeline Navarro,
Santiago Segarra,
and
Antonio G. Marques,
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
2022
Network Topology Inference with Graphon Spectral Penalties [arXiv]
T. Mitchell Roddenberry,
Madeline Navarro,
and
Santiago Segarra,
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
2021
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