Network Topology Inference with Graphon Spectral Penalties. - T. Mitchell Roddenberry; Madeline Navarro; Santiago Segarra (ICASSP, 2021)

Network Clustering for Latent State and Changepoint Detection. - Madeline Navarro; Genevera I. Allen; Michael Weylandt (CoRR, 2021)

Joint graph learning from Gaussian observations in the presence of hidden nodes. - Samuel Rey; Madeline Navarro; Andrei Buciulea; Santiago Segarra; Antonio G. Marques (IEEECONF, 2022)

Graphon-Aided Joint Estimation of Multiple Graphs. - Madeline Navarro; Santiago Segarra (ICASSP, 2022)

Joint Inference of Multiple Graphs with Hidden Variables from Stationary Graph Signals. - Samuel Rey; Andrei Buciulea; Madeline Navarro; Santiago Segarra; Antonio G. Marques (ICASSP, 2022)

Joint Inference of Multiple Graphs from Matrix Polynomials. - Madeline Navarro; Yuhao Wang 005; Antonio G. Marques; Caroline Uhler; Santiago Segarra (J. Mach. Learn. Res., 2022)

Joint Network Topology Inference Via a Shared Graphon Model. - Madeline Navarro; Santiago Segarra (IEEE Trans. Signal Process., 2022)

Graphmad: Graph Mixup for Data Augmentation Using Data-Driven Convex Clustering. - Madeline Navarro; Santiago Segarra (ICASSP, 2023)

Structure-Guided Input Graph for GNNs Facing Heterophily. - Victor M. Tenorio; Madeline Navarro; Samuel Rey; Santiago Segarra; Antonio G. Marques (IEEECONF, 2024)

Mitigating Subpopulation Bias for Fair Network Topology Inference. - Madeline Navarro; Samuel Rey; Andrei Buciulea; Antonio G. Marques; Santiago Segarra (EUSIPCO, 2024)

Data Augmentation via Subgroup Mixup for Improving Fairness. - Madeline Navarro; Camille Olivia Little; Genevera I. Allen; Santiago Segarra (ICASSP, 2024)

SC-MAD: Mixtures of Higher-Order Networks for Data Augmentation. - Madeline Navarro; Santiago Segarra (ICASSP, 2024)

Recovering Missing Node Features with Local Structure-Based Embeddings. - Victor M. Tenorio; Madeline Navarro; Santiago Segarra; Antonio G. Marques (ICASSP, 2024)

Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior. - Madeline Navarro; Samuel Rey; Andrei Buciulea; Antonio G. Marques; Santiago Segarra (NeurIPS, 2024)

ML-SPEAK: A Theory-Guided Machine Learning Method for Studying and Predicting Conversational Turn-taking Patterns. - Lisa O'Bryan; Madeline Navarro; Juan Segundo Hevia; Santiago Segarra (CoRR, 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 (ICASSP, 2025)

Fair CoVariance Neural Networks. - Andrea Cavallo; Madeline Navarro; Santiago Segarra; Elvin Isufi (ICASSP, 2025)

Bayesian Filtering on Graphs. - Bishwadeep Das; Madeline Navarro; Santiago Segarra; Elvin Isufi (ICASSP, 2025)

Low-Rank Tensors for Multi-Dimensional Markov Models. - Madeline Navarro; Sergio Rozada; Antonio G. Marques; Santiago Segarra (ICASSP, 2025)

Redesigning graph filter-based GNNs to relax the homophily assumption. - Samuel Rey; Madeline Navarro; Victor M. Tenorio; Santiago Segarra; Antonio G. Marques (ICASSP, 2025)

Adapting to Heterophilic Graph Data with Structure-Guided Neighbor Discovery. - Victor M. Tenorio; Madeline Navarro; Samuel Rey; Santiago Segarra; Antonio G. Marques (CoRR, 2025)