The construction of a meaningful graph topology plays a crucial role in the effective
representation, processing, analysis, and visualization of structured data. When a natural …

Graph learning from data is a canonical problem that has received substantial attention in
the literature. Learning a structured graph is essential for interpretability and identification of …

Learning a graph with a specific structure is essential for interpretability and identification of
the relationships among data. But structured graph learning from observed samples is an …

Recent papers have formulated the problem of learning graphs from data as an inverse
covariance estimation problem with graph Laplacian constraints. While such problems are …

We consider the problem of learning a graph from a given set of smooth graph signals. Our
graph learning approach is formulated as a constrained quadratic program in the edge …

We propose a method to maintain high resource availability in a networked heterogeneous
multi-robot system subject to resource failures. In our model, resources such as sensing and …

We propose a framework for resilience in a networked heterogeneous multirobot team
subject to resource failures. Each robot in the team is equipped with resources that it shares …

We address the problem of maintaining resource availability in a networked multirobot team
performing distributed tracking of an unknown number of targets in a bounded environment …

Spatial regression models are widely used in numerous areas, including detecting and
predicting traffic volume, air pollution, and housing prices. Unlike conventional regression …

Graph signal processing (GSP) has provided a wide range of powerful methodologies for
diverse learning tasks. While the data domain in GSP is fundamentally non-Euclidean, the …