Madeline Navarro

According to our database, Madeline Navarro authored at least 17 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Joint Network Topology Inference in the Presence of Hidden Nodes.

Madeline Navarro, Samuel Rey, Andrei Buciulea, Antonio G. Marques, Santiago Segarra

IEEE Trans. Signal Process., 2024

Online Network Inference from Graph-Stationary Signals with Hidden Nodes.

Andrei Buciulea, Madeline Navarro, Samuel Rey, Santiago Segarra, Antonio G. Marques

CoRR, 2024

Redesigning graph filter-based GNNs to relax the homophily assumption.

Samuel Rey, Madeline Navarro, Victor M. Tenorio, Santiago Segarra, Antonio G. Marques

CoRR, 2024

Fair CoVariance Neural Networks.

Andrea Cavallo, Madeline Navarro, Santiago Segarra, Elvin Isufi

CoRR, 2024

Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior.

Madeline Navarro, Samuel Rey, Andrei Buciulea, Antonio G. Marques, Santiago Segarra

CoRR, 2024

Recovering Missing Node Features with Local Structure-Based Embeddings.

Victor M. Tenorio, Madeline Navarro, Santiago Segarra, Antonio G. Marques

Proceedings of the IEEE International Conference on Acoustics, 2024

SC-MAD: Mixtures of Higher-Order Networks for Data Augmentation.

Madeline Navarro, Santiago Segarra

Proceedings of the IEEE International Conference on Acoustics, 2024

Data Augmentation via Subgroup Mixup for Improving Fairness.

Madeline Navarro, Camille Olivia Little, Genevera I. Allen, Santiago Segarra

Proceedings of the IEEE International Conference on Acoustics, 2024

Mitigating Subpopulation Bias for Fair Network Topology Inference.

Madeline Navarro, Samuel Rey, Andrei Buciulea, Antonio G. Marques, Santiago Segarra

Proceedings of the 32nd European Signal Processing Conference, 2024

2023

Graphmad: Graph Mixup for Data Augmentation Using Data-Driven Convex Clustering.

Madeline Navarro, Santiago Segarra

Proceedings of the IEEE International Conference on Acoustics, 2023

2022

Joint Network Topology Inference Via a Shared Graphon Model.

Madeline Navarro, Santiago Segarra

IEEE Trans. Signal Process., 2022

Joint Inference of Multiple Graphs from Matrix Polynomials.

Madeline Navarro, Yuhao Wang, Antonio G. Marques, Caroline Uhler, Santiago Segarra

J. Mach. Learn. Res., 2022

Joint Inference of Multiple Graphs with Hidden Variables from Stationary Graph Signals.

Samuel Rey, Andrei Buciulea, Madeline Navarro, Santiago Segarra, Antonio G. Marques

Proceedings of the IEEE International Conference on Acoustics, 2022

Graphon-Aided Joint Estimation of Multiple Graphs.

Madeline Navarro, Santiago Segarra

Proceedings of the IEEE International Conference on Acoustics, 2022

Joint graph learning from Gaussian observations in the presence of hidden nodes.

Samuel Rey, Madeline Navarro, Andrei Buciulea, Santiago Segarra, Antonio G. Marques

Proceedings of the 56th Asilomar Conference on Signals, Systems, and Computers, ACSSC 2022, Pacific Grove, CA, USA, October 31, 2022

2021

Network Clustering for Latent State and Changepoint Detection.

Madeline Navarro, Genevera I. Allen, Michael Weylandt

CoRR, 2021

Network Topology Inference with Graphon Spectral Penalties.

T. Mitchell Roddenberry, Madeline Navarro, Santiago Segarra

Proceedings of the IEEE International Conference on Acoustics, 2021