activity
20182020
most citedHow Does Momentum Help Frank Wolfe?

3 citations · 3 across the 3 of their papers we have counts for

collaborators

13 papers

eess.SP2020

Node-Adaptive Regularization for Graph Signal Reconstruction

Maosheng Yang, Mario Coutino, Geert Leus +1

A critical task in graph signal processing is to estimate the true signal from noisy observations over a subset of nodes, also known as the reconstruction problem. In this paper, w…

eess.SP2020

Online Time-Varying Topology Identification via Prediction-Correction Algorithms

Alberto Natali, Mario Coutino, Elvin Isufi +1

Signal processing and machine learning algorithms for data supported over graphs, require the knowledge of the graph topology. Unless this information is given by the physics of th…

eess.SP2020

Topology-Aware Joint Graph Filter and Edge Weight Identification for Network Processes

Alberto Natali, Mario Coutino, Geert Leus

Data defined over a network have been successfully modelled by means of graph filters. However, although in many scenarios the connectivity of the network is known, e.g., smart gri…

math.OC20203 cited

How Does Momentum Help Frank Wolfe?

Bingcong Li, Mario Coutino, Georgios B. Giannakis +1

We unveil the connections between Frank Wolfe (FW) type algorithms and the momentum in Accelerated Gradient Methods (AGM). On the negative side, these connections illustrate why mo…

eess.SP2020

Joint blind calibration and time-delay estimation for multiband ranging

Tarik Kazaz, Mario Coutino, Gerard J. M. Janssen +1

In this paper, we focus on the problem of blind joint calibration of multiband transceivers and time-delay (TD) estimation of multipath channels. We show that this problem can be f…

eess.SP2019

State-Space Based Network Topology Identification

Mario Coutino, Elvin Isufi, Takanori Maehara +1

In this work, we explore the state-space formulation of network processes to recover the underlying structure of the network (local connections). To do so, we employ subspace techn…