Publications by Volkan Cevher | EPFL Graph Search

How little does non-exact recovery help in group testing?

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 tests. We characterize the information-theoretic performance limits in the presence of list decoding, in whi ...

Ieee2017

Learning-based subsampling

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

The present invention concerns a method of sampling a test signal. The method comprises: acquiring (21) training signals sampled at a plurality of sampling locations; running (23) an optimization procedure for determining an index set of n indices, represe ...

2017

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

Combinatorial Penalties: Which structures are preserved by convex relaxations?

Volkan Cevher, Marwa El Halabi

We consider the homogeneous and the non-homogeneous convex relaxations for combinatorial penalty functions defined on support sets. Our study identifies key differences in the tightness of the resulting relaxations through the notion of the lower combinato ...

2017

Phase Transitions in the Pooled Data Problem

Volkan Cevher, Jonathan Mark Scarlett

In this paper, we study the {\em pooled data} problem of identifying the labels associated with a large collection of items, based on a sequence of pooled tests revealing the counts of each label within the pool. In the noiseless setting, we identify an ex ...

2017

Group-Sparse Model Selection: Hardness and Relaxations

Volkan Cevher, Anastasios Kyrillidis, 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. The main promise of these models is the recovery of ``interpretable" signals along with ...

Institute of Electrical and Electronics Engineers2016

Convex block-sparse linear regression with expanders - provably

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis, Luca Baldassarre, Bubacarr Bah

Sparse matrices are favorable objects in machine learning and optimization. When such matrices are used, in place of dense ones, the overall complexity requirements in optimization can be significantly reduced in practice, both in terms of space and run-ti ...

2016

An Efficient Streaming Algorithm for the Submodular Cover Problem

Volkan Cevher, Ilija Bogunovic, Ya-Ping Hsieh, Ashkan Norouzi Fard, Abbas Bazzi, Marwa El Halabi

We initiate the study of the classical Submodular Cover (SC) problem in the data streaming model which we refer to as the Streaming Submodular Cover (SSC). We show that any single pass streaming algorithm using sublinear memory in the size of the stream wi ...

2016

Stochastic Three-Composite Convex Minimization

Volkan Cevher, Alp Yurtsever, Cong Bang Vu

We propose a stochastic optimization method for the minimization of the sum of three convex functions, one of which has Lipschitz continuous gradient as well as restricted strong convexity. Our approach is most suitable in the setting where it is computati ...

2016

Learning-Based Near-Optimal Area-Power Trade-offs in Hardware Design for Neural Signal Acquisition

Volkan Cevher, Mahsa Shoaran, Cosimo Aprile, Yusuf Leblebici, Luca Baldassarre, Vipul Gupta

Wireless implantable devices capable of monitoring the electrical activity of the brain are becoming an important tool for understanding and potentially treating mental diseases such as epilepsy and depression. While such devices exist, it is still necessa ...

Ieee2016

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How little does non-exact recovery help in group testing?

Learning-based subsampling

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Combinatorial Penalties: Which structures are preserved by convex relaxations?

Phase Transitions in the Pooled Data Problem

Group-Sparse Model Selection: Hardness and Relaxations

Convex block-sparse linear regression with expanders - provably

An Efficient Streaming Algorithm for the Submodular Cover Problem

Stochastic Three-Composite Convex Minimization

Learning-Based Near-Optimal Area-Power Trade-offs in Hardware Design for Neural Signal Acquisition

Convex block-sparse linear regression with expanders - provably

How little does non-exact recovery help in group testing?

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Phase Transitions in the Pooled Data Problem

Group-Sparse Model Selection: Hardness and Relaxations

Learning-based subsampling

Combinatorial Penalties: Which structures are preserved by convex relaxations?

An Efficient Streaming Algorithm for the Submodular Cover Problem

Stochastic Three-Composite Convex Minimization

Learning-Based Near-Optimal Area-Power Trade-offs in Hardware Design for Neural Signal Acquisition