Publications by Volkan Cevher | EPFL Graph Search

Binary Sparse Coding of Convolutive Mixtures for Sound Localization and Separation via Spatialization

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei, Mohammadjavad Taghizadeh, Saeid Haghighatshoar

We propose a sparse coding approach to address the problem of source-sensor localization and speech reconstruction. This approach relies on designing a dictionary of spatialized signals by projecting the microphone array recordings into the array manifolds ...

Institute of Electrical and Electronics Engineers2016

Learning-Based Compressive Subsampling

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic, Yen-Huan Li, Luca Baldassarre, Baran Gözcü

The problem of recovering a structured signal

\mathbf{x} \in \mathbb{C}^p

from a set of dimensionality-reduced linear measurements

\mathbf{b} = \mathbf {A}\mathbf {x}

arises in a variety of applications, such as medical imaging, spectroscopy, Fourier o ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices

Volkan Cevher, Jonathan Mark Scarlett

Linear sketching is a powerful tool for the problem of sparse signal recovery, having numerous applications such as compressive sensing, data stream computing, graph sketching, and routing. Motivated by applications where the \emph{positions} of the non-ze ...

2016

On the Difficulty of Selecting Ising Models with Approximate Recovery

Volkan Cevher, Jonathan Mark Scarlett

In this paper, we consider the problem of estimating the underlying graph associated with an Ising model given a number of independent and identically distributed samples. We adopt an approximate recovery criterion that allows for a number of missed edges ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Converse Bounds for Noisy Group Testing with Arbitrary Measurement Matrices

Volkan Cevher, Jonathan Mark Scarlett

We consider the group testing problem, in which one seeks to identify a subset of defective items within a larger set of items based on a number of noisy tests. While matching achievability and converse bounds are known in several cases of interest for i.i ...

Ieee2016

Partial Recovery Bounds for the Sparse Stochastic Block Model

Volkan Cevher, Jonathan Mark Scarlett

In this paper, we study the information-theoretic limits of community detection in the symmetric two-community stochastic block model, with intra-community and inter-community edge probabilities

\frac{a}{n}

and

\frac{b}{n}

respectively. We consider the ...

Ieee2016

Frank-Wolfe Works for Non-Lipschitz Continuous Gradient Objectives: Scalable Poisson Phase Retrieval

Volkan Cevher, Quoc Tran Dinh, Alp Yurtsever, Yen-Huan Li, Ya-Ping Hsieh, Marwa El Halabi, Gergely Odor

We study a phase retrieval problem in the Poisson noise model. Motivated by the PhaseLift approach, we approximate the maximum-likelihood estimator by solving a convex program with a nuclear norm constraint. While the Frank-Wolfe algorithm, together with t ...

Ieee2016

Learning Data Triage: Linear Decoding Works for Compressive MRI

Volkan Cevher, Yen-Huan Li

The standard approach to compressive sampling considers recovering an unknown deterministic signal with certain known structure, and designing the sub-sampling pattern and recovery algorithm based on the known structure. This approach requires looking for ...

Ieee2016

Signal Recovery and System Calibration from Multiple Compressive Poisson Measurements

Volkan Cevher, Xin Yuan

The measurement matrix employed in compressive sensing typically cannot be known precisely a priori, and must be estimated via calibration. One may take multiple compressive measurements, from which the measurement matrix and underlying signals may be esti ...

Siam Publications2015

Sparse Group Covers and Greedy Tree Approximations

Volkan Cevher, Luca Baldassarre

We consider the problem of finding a K-sparse approximation of a signal, such that the support of the approximation is the union of sets from a given collection, a.k.a. group structure. This problem subsumes the one of finding K-sparse tree approximations. ...

2015

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Binary Sparse Coding of Convolutive Mixtures for Sound Localization and Separation via Spatialization

Learning-Based Compressive Subsampling

Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices

On the Difficulty of Selecting Ising Models with Approximate Recovery

Converse Bounds for Noisy Group Testing with Arbitrary Measurement Matrices

Partial Recovery Bounds for the Sparse Stochastic Block Model

Frank-Wolfe Works for Non-Lipschitz Continuous Gradient Objectives: Scalable Poisson Phase Retrieval

Learning Data Triage: Linear Decoding Works for Compressive MRI

Signal Recovery and System Calibration from Multiple Compressive Poisson Measurements

Sparse Group Covers and Greedy Tree Approximations

Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices

Signal Recovery and System Calibration from Multiple Compressive Poisson Measurements

Learning Data Triage: Linear Decoding Works for Compressive MRI

Frank-Wolfe Works for Non-Lipschitz Continuous Gradient Objectives: Scalable Poisson Phase Retrieval

Learning-Based Compressive Subsampling

On the Difficulty of Selecting Ising Models with Approximate Recovery

Binary Sparse Coding of Convolutive Mixtures for Sound Localization and Separation via Spatialization

Partial Recovery Bounds for the Sparse Stochastic Block Model

Converse Bounds for Noisy Group Testing with Arbitrary Measurement Matrices

Sparse Group Covers and Greedy Tree Approximations