Publications by Volkan Cevher | EPFL Graph Search

Model-based Sparse Component Analysis for Reverberant Speech Localization

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei, Mohammadjavad Taghizadeh

This paper proposes a speech localization framework based on model-based sparse recovery. We compare and contrast the computational sparse optimization methods incorporating harmonicity and block structures as well as autoregressive dependencies underlying ...

IEEE2014

Tractability of interpretability via selection of group-sparse models

Volkan Cevher, Luca Baldassarre, Nirav Bhan

Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing. A promise of these models is to lead to “interpretable” signals for which we identify i ...

2013

Energy-aware adaptive bi-Lipschitz embeddings

Volkan Cevher, Bubacarr Bah, Ali Sadeghian

We propose a dimensionality reducing matrix design based on training data with constraints on its Frobenius norm and number of rows. Our design criteria is aimed at preserving the distances between the data points in the dimensionality reduced space as muc ...

2013

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis

We propose an algorithmic framework for convex minimization problems of a composite function with two terms: a self-concordant function and a possibly nonsmooth regularization term. Our method is a new proximal Newton algorithm that features a local quadra ...

2013

To Convexify or Not? Regression with Clustering Penalties on Graphs

Volkan Cevher, Luca Baldassarre, Marwa El Halabi

We consider minimization problems that are compositions of convex functions of a vector

\x \in\reals^N

with submodular set functions of its support (i.e., indices of the non-zero coefficients of

\x

). Such problems are in general difficult for large

N

...

IEEE2013

Sparse projections onto the simplex

Volkan Cevher, Christoph Koch, Anastasios Kyrillidis

Most learning methods with rank or sparsity constraints use convex relaxations, which lead to optimization with the nuclear norm or the`1-norm. However, several important learning applications cannot benet from this approach as they feature these convex no ...

JMLR W&CP2013

Randomized Low-Memory Singular Value Projection

Volkan Cevher, Anastasios Kyrillidis

Affine rank minimization algorithms typically rely on calculating the gradient of a data error followed by a singular value decomposition at every iteration. Because these two steps are expensive, heuristics are often used for approximations that reduce co ...

2013

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Volkan Cevher, Anastasios Kyrillidis

The convex

\ell_1

-regularized

\log\det

divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the

\ell_1

-regularization is nonsmooth, the

\log\det

objective is n ...

2013

Manifold Sparse Beamforming

Volkan Cevher, Afsaneh Asaei, Baran Gözcü

We consider the minimum variance distortionless response (MVDR) beamforming problems where the array covariance matrix is rank deficient. The conventional approach handles such rank-deficiencies via diagonal loading on the covariance matrix. In this settin ...

2013

High-Dimensional Gaussian Process Bandits

Volkan Cevher, Andreas Krause

Many applications in machine learning require optimizing unknown functions defined over a high-dimensional space from noisy samples that are expensive to obtain. We address this notoriously hard challenge, under the assumptions that the function varies onl ...

2013

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Model-based Sparse Component Analysis for Reverberant Speech Localization

Tractability of interpretability via selection of group-sparse models

Energy-aware adaptive bi-Lipschitz embeddings

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

To Convexify or Not? Regression with Clustering Penalties on Graphs

Sparse projections onto the simplex

Randomized Low-Memory Singular Value Projection

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Manifold Sparse Beamforming

High-Dimensional Gaussian Process Bandits

To Convexify or Not? Regression with Clustering Penalties on Graphs

Tractability of interpretability via selection of group-sparse models

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Randomized Low-Memory Singular Value Projection

Manifold Sparse Beamforming

Model-based Sparse Component Analysis for Reverberant Speech Localization

Energy-aware adaptive bi-Lipschitz embeddings

Sparse projections onto the simplex

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

High-Dimensional Gaussian Process Bandits