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

Advancing the State of Network Switch ASIC Offloading in the Linux Kernel

Modern data-center network operating systems rely on proprietary user-space daemons wrapping SDKs from switch vendors. Linux-based variants of these operating systems have benefited from increasing and simplified dataplane offloading support in recent year ...

2018

The Mutual Information in Random Linear Estimation Beyond i.i.d. Matrices

Nicolas Macris, Florent Gérard Krzakala, Jean François Emmanuel Barbier

There has been definite progress recently in proving the variational single-letter formula given by the heuristic replica method for various estimation problems. In particular, the replica formula for the mutual information in the case of noisy linear esti ...

IEEE2018

Lower Bounds on Active Learning for Graphical Model Selection

Volkan Cevher, Jonathan Mark Scarlett

We consider the problem of estimating the underlying graph associated with a Markov random field, with the added twist that the decoding algorithm can iteratively choose which subsets of nodes to sample based on the previous samples, resulting in an active ...

2017

Functional data analysis by matrix completion

Marie-Hélène Descary

Traditional approaches to analysing functional data typically follow a two-step procedure, consisting in first smoothing and then carrying out a functional principal component analysis. The idea underlying this procedure is that functional data are well ap ...

EPFL2017

When Slepian Meets Fiedler: Putting a Focus on the Graph Spectrum

Dimitri Nestor Alice Van De Ville, Maria Giulia Preti, Robin Jonathan Demesmaeker

The study of complex systems greatly benefits from graph models and their analysis. In particular, the eigendecomposition of the graph Laplacian lets emerge properties of global organization from local interactions; e.g., the Fiedler vector has the smalles ...

Institute of Electrical and Electronics Engineers2017

A Prediction-Error Covariance Estimator for Adaptive Kalman Filtering in Step-Varying Processes: Application to Power-System State Estimation

Jean-Yves Le Boudec, Mario Paolone, Lorenzo Zanni

In this paper, we present a new method for the estimation of the prediction-error covariances of a Kalman filter (KF), which is suitable for step-varying processes. The method uses a series of past innovations (i.e., the difference between the upcoming mea ...

Institute of Electrical and Electronics Engineers2017

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic

In this paper, we consider the problem of sequentially optimizing a black-box function

f

based on noisy samples and bandit feedback. We assume that

f

is smooth in the sense of having a bounded norm in some reproducing kernel Hilbert space (RKHS), yield ...

2017

Tracking Time-Vertex Propagation using Dynamic Graph Wavelets

Nathanaël Perraudin, Benjamin Ricaud, Francesco Grassi

Graph Signal Processing generalizes classical signal processing to signal or data indexed by the vertices of a weighted graph. So far, the research efforts have been focused on static graph signals. However numerous applications involve graph signals evolv ...

2016

Graph-Based Representation and Coding of Multiview Images

Pascal Frossard, Bénédicte Motz

Instead of lossily coding depth images resulting in undesirable geometric distortion, graph-based representation (GBR) describes disparity information as a graph with a controllable accuracy. In this paper, we propose a more compact graphical representatio ...

Ieee2016

Graph Signal Processing

Ntorina Thanou

Over the past few decades we have been experiencing an explosion of information generated by large networks of sensors and other data sources. Much of this data is intrinsically structured, such as traffic evolution in a transportation network, temperature ...

EPFL2016

Deconvolution of Gaussian Random Fields using the Graph Fourier Transform

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Advancing the State of Network Switch ASIC Offloading in the Linux Kernel

The Mutual Information in Random Linear Estimation Beyond i.i.d. Matrices

Lower Bounds on Active Learning for Graphical Model Selection

Functional data analysis by matrix completion

When Slepian Meets Fiedler: Putting a Focus on the Graph Spectrum

A Prediction-Error Covariance Estimator for Adaptive Kalman Filtering in Step-Varying Processes: Application to Power-System State Estimation

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Tracking Time-Vertex Propagation using Dynamic Graph Wavelets

Graph-Based Representation and Coding of Multiview Images

Graph Signal Processing

Advancing the State of Network Switch ASIC Offloading in the Linux Kernel

The Mutual Information in Random Linear Estimation Beyond i.i.d. Matrices

When Slepian Meets Fiedler: Putting a Focus on the Graph Spectrum

Lower Bounds on Active Learning for Graphical Model Selection

Functional data analysis by matrix completion

A Prediction-Error Covariance Estimator for Adaptive Kalman Filtering in Step-Varying Processes: Application to Power-System State Estimation

Tracking Time-Vertex Propagation using Dynamic Graph Wavelets

Graph Signal Processing

Graph-Based Representation and Coding of Multiview Images

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization