Publications related to Deconvolution of Gaussian Random Fields using the Graph Fourier Transform

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The Completion Of Covariance Kernels

Victor Panaretos, Kartik Waghmare

We consider the problem of positive-semidefinite continuation: extending a partially specified covariance kernel from a subdomain Omega of a rectangular domain I x I to a covariance kernel on the entire domain I x I. For a broad class of domains Omega call ...

INST MATHEMATICAL STATISTICS-IMS2022

Figlearn: Filter And Graph Learning Using Optimal Transport

Pascal Frossard, Mireille El Gheche, Matthias Minder, Zahra Farsijani

In many applications, a dataset can be considered as a set of observed signals that live on an unknown underlying graph structure. Some of these signals may be seen as white noise that has been filtered on the graph topology by a graph filter. Hence, the k ...

IEEE2021

Online Distributed Learning Over Graphs With Multitask Graph-Filter Models

Ali H. Sayed, Roula Nassif

In this article, we are interested in adaptive and distributed estimation of graph filters from streaming data. We formulate this problem as a consensus estimation problem over graphs, which can be addressed with diffusion LMS strategies. Most popular grap ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Graph Laplacian Mixture Model

Pascal Frossard, Hermina Petric Maretic

Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all avai ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Flooding And Diameter In General Weighted Random Graphs

Thomas Mountford, Jacques Saliba

In this paper we study first passage percolation on a random graph model, the configuration model. We first introduce the notions of weighted diameter, which is the maximum of the weighted lengths of all optimal paths between any two vertices in the graph, ...

CAMBRIDGE UNIV PRESS2020

Certified And Fast Computations With Shallow Covariance Kernels

Daniel Kressner, Stefano Massei

Many techniques for data science and uncertainty quantification demand efficient tools to handle Gaussian random fields, which are defined in terms of their mean functions and covariance operators. Recently, parameterized Gaussian random fields have gained ...

AMER INST MATHEMATICAL SCIENCES-AIMS2020

Joint Graph-based Depth Refinement and Normal Estimation

Pascal Frossard, Mireille El Gheche, Mattia Rossi

Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings. These scenes are characterized by a prevalence of human made structures, which in most of the cases, are either ...

2020

USBFuzz: A Framework for Fuzzing USB Drivers by Device Emulation

Mathias Josef Payer, Hui Peng

The Universal Serial Bus (USB) connects external devices to a host. This interface exposes the OS kernels and device drivers to attacks by malicious devices. Unfortunately, kernels and drivers were developed under a security model that implicitly trusts co ...

USENIX ASSOC2020

Deconvolution of Gaussian Random Fields using the Graph Fourier Transform

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The Completion Of Covariance Kernels

Figlearn: Filter And Graph Learning Using Optimal Transport

Online Distributed Learning Over Graphs With Multitask Graph-Filter Models

Graph Laplacian Mixture Model

Flooding And Diameter In General Weighted Random Graphs

Non-Exponential Variations for Classical Results in First Passage Percolation

Certified And Fast Computations With Shallow Covariance Kernels

Joint Graph-based Depth Refinement and Normal Estimation

Graph-based regularization of inverse problems in imaging

USBFuzz: A Framework for Fuzzing USB Drivers by Device Emulation

The Completion Of Covariance Kernels

Figlearn: Filter And Graph Learning Using Optimal Transport

Graph-based regularization of inverse problems in imaging

Flooding And Diameter In General Weighted Random Graphs

Online Distributed Learning Over Graphs With Multitask Graph-Filter Models

Non-Exponential Variations for Classical Results in First Passage Percolation

Joint Graph-based Depth Refinement and Normal Estimation

Certified And Fast Computations With Shallow Covariance Kernels

Graph Laplacian Mixture Model

USBFuzz: A Framework for Fuzzing USB Drivers by Device Emulation