60 citations · 104 across the 47 of their papers we have counts for
45 papers
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…
Deep Unfolding: Recent Developments, Theory, and Design Guidelines
Nir Shlezinger, Santiago Segarra, Yi Zhang +4
Optimization methods play a central role in signal processing, serving as the mathematical foundation for inference, estimation, and control. While classical iterative optimization…
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…
Delay-aware Backpressure Routing Using Graph Neural Networks
Zhongyuan Zhao, Bojan Radojicic, Gunjan Verma +2
We propose a throughput-optimal biased backpressure (BP) algorithm for routing, where the bias is learned through a graph neural network that seeks to minimize end-to-end delay. Cl…
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…
Accelerated massive MIMO detector based on annealed underdamped Langevin dynamics
Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady +2
We propose a multiple-input multiple-output (MIMO) detector based on an annealed version of the \emph{underdamped} Langevin (stochastic) dynamic. Our detector achieves state-of-the…