385 citations · 2k across the 117 of their papers we have counts for
11 papers · 1 filter
Directed Graph Topology Inference via Graph Filter Identification
Rasoul Shafipour, Andrei Buciulea, Santiago Segarra +2
We address the problem of inferring a directed network from nodal measurements generated by linear diffusion dynamics on the sought graph. Observations are modeled as the outputs o…
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…
Estimation of partially known Gaussian graphical models with score-based structural priors
Martín Sevilla, Antonio García Marques, Santiago Segarra
We propose a novel algorithm for the support estimation of partially known Gaussian graphical models that incorporates prior information about the underlying graph. In contrast 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…
Solving Linear Inverse Problems using Higher-Order Annealed Langevin Diffusion
Nicolas Zilberstein, Ashutosh Sabharwal, Santiago Segarra
We propose a solution for linear inverse problems based on higher-order Langevin diffusion. More precisely, we propose pre-conditioned second-order and third-order Langevin dynamic…
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…
Graphon-aided Joint Estimation of Multiple Graphs
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…
Rank-One Measurements of Low-Rank PSD Matrices Have Small Feasible Sets
T. Mitchell Roddenberry, Santiago Segarra, Anastasios Kyrillidis
We study the role of the constraint set in determining the solution to low-rank, positive semidefinite (PSD) matrix sensing problems. The setting we consider involves rank-one sens…
Network topology change-point detection from graph signals with prior spectral signatures
Chiraag Kaushik, T. Mitchell Roddenberry, Santiago Segarra
We consider the problem of sequential graph topology change-point detection from graph signals. We assume that signals on the nodes of the graph are regularized by the underlying g…
Joint Inference of Multiple Graphs from Matrix Polynomials
Madeline Navarro, Yuhao Wang, Antonio G. Marques +2
Inferring graph structure from observations on the nodes is an important and popular network science task. Departing from the more common inference of a single graph and motivated…