Publications by Volkan Cevher | EPFL Graph Search

A single-phase, proximal path-following framework

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis

We propose a new proximal, path-following framework for a class of---possibly non-smooth---constrained convex problems. We consider settings where the non-smooth part is endowed with a proximity operator, and the constraint set is equipped with a self-conc ...

INFORMS2017

Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage

Volkan Cevher, Alp Yurtsever

This paper considers a fundamental class of convex matrix optimization problems with low-rank solutions. We show it is possible to solve these problem using far less memory than the natural size of the decision variable when the problem data has a concise ...

2017

DCT Learning-Based Hardware Design for Neural Signal Acquisition Systems

Volkan Cevher, Cosimo Aprile, Yusuf Leblebici, Luca Baldassarre

This work presents an area and power efficient encoding system for wireless implantable devices capable of monitoring the electrical activity of the brain. Such devices are becoming an important tool for understanding, real-time monitoring, and potentially ...

2017

Robust Submodular Maximization: A Non-Uniform Partitioning Approach

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic, Slobodan Mitrovic

We study the problem of maximizing a monotone submodular function subject to a cardinality constraint

k

, with the added twist that a number of items

\tau

from the returned set may be removed. We focus on the worst-case setting considered in Orlin et al ...

2017

A Distributed Algorithm for Partitioned Robust Submodular Maximization

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic, Slobodan Mitrovic

Abstract—In this paper, we consider the problem of maximizing a monotone submodular function subject to a cardinality constraint, with two added twists: The computation is distributed across a number of machines, and we require the solution to be robust ag ...

2017

Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization

Volkan Cevher, Quoc Tran Dinh, Ahmet Alacaoglu

We propose a new randomized coordinate descent method for a convex optimization template with broad applications. Our analysis relies on a novel combination of four ideas applied to the primal-dual gap function: smoothing, acceleration, homotopy, and coord ...

2017

Practical Sketching Algorithms For Low-Rank Matrix Approximation

Volkan Cevher, Alp Yurtsever

This paper describes a suite of algorithms for constructing low-rank approximations of an input matrix from a random linear image, or sketch, of the matrix. These methods can preserve structural properties of the input matrix, such as positive-semidefinite ...

Society for Industrial and Applied Mathematics2017

Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data

Volkan Cevher, Alp Yurtsever

Several important applications, such as streaming PCA and semidefinite programming, involve a large-scale positive-semidefinite (psd) matrix that is presented as a sequence of linear updates. Because of storage limitations, it may only be possible to retai ...

2017

Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework

Volkan Cevher, Jonathan Mark Scarlett

The support recovery problem consists of determining a sparse subset of a set of variables that is relevant in generating a set of observations, and arises in a diverse range of settings, such as compressive sensing, subset selection in regression, and gro ...

Institute of Electrical and Electronics Engineers2017

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

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A single-phase, proximal path-following framework

Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage

DCT Learning-Based Hardware Design for Neural Signal Acquisition Systems

Robust Submodular Maximization: A Non-Uniform Partitioning Approach

A Distributed Algorithm for Partitioned Robust Submodular Maximization

Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization

Practical Sketching Algorithms For Low-Rank Matrix Approximation

Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data

Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework

Lower Bounds on Active Learning for Graphical Model Selection

A single-phase, proximal path-following framework

Lower Bounds on Active Learning for Graphical Model Selection

Robust Submodular Maximization: A Non-Uniform Partitioning Approach

Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage

DCT Learning-Based Hardware Design for Neural Signal Acquisition Systems

Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization

A Distributed Algorithm for Partitioned Robust Submodular Maximization

Practical Sketching Algorithms For Low-Rank Matrix Approximation

Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data

Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework