1.3k citations · 3.8k across the 222 of their papers we have counts for
10 papers · 1 filter
Learning-based Compressive Subsampling
Luca Baldassarre, Yen-Huan Li, Jonathan Scarlett +3
The problem of recovering a structured signal from a set of dimensionality-reduced linear measurements arises in…
Linear Inverse Problems with Norm and Sparsity Constraints
Volkan Cevher, Sina Jafarpour, Anastasios Kyrillidis
We describe two nonconventional algorithms for linear regression, called GAME and CLASH. The salient characteristics of these approaches is that they exploit the convex -ba…
Structured Sparsity: Discrete and Convex approaches
Anastasios Kyrillidis, Luca Baldassarre, Marwa El-Halabi +2
Compressive sensing (CS) exploits sparsity to recover sparse or compressible signals from dimensionality reducing, non-adaptive sensing mechanisms. Sparsity is also used to enhance…
A Smooth Primal-Dual Optimization Framework for Nonsmooth Composite Convex Minimization
Quoc Tran-Dinh, Olivier Fercoq, Volkan Cevher
We propose a new first-order primal-dual optimization framework for a convex optimization template with broad applications. Our optimization algorithms feature optimal convergence…
Smooth Alternating Direction Methods for Nonsmooth Constrained Convex Optimization
Quoc Tran-Dinh, Volkan Cevher
We propose two new alternating direction methods to solve "fully" nonsmooth constrained convex problems. Our algorithms have the best known worst-case iteration-complexity guarante…
A Geometric View on Constrained M-Estimators
Yen-Huan Li, Ya-Ping Hsieh, Nissim Zerbib +1
We study the estimation error of constrained M-estimators, and derive explicit upper bounds on the expected estimation error determined by the Gaussian width of the constraint set.…
Adaptive-Rate Sparse Signal Reconstruction With Application in Compressive Background Subtraction
Joao F. C. Mota, Nikos Deligiannis, Aswin C. Sankaranarayanan +2
We propose and analyze an online algorithm for reconstructing a sequence of signals from a limited number of linear measurements. The signals are assumed sparse, with unknown suppo…
A Universal Primal-Dual Convex Optimization Framework
Alp Yurtsever, Quoc Tran-Dinh, Volkan Cevher
We propose a new primal-dual algorithmic framework for a prototypical constrained convex optimization template. The algorithmic instances of our framework are universal since they…
Composite convex minimization involving self-concordant-like cost functions
Quoc Tran-Dinh, Yen-Huan Li, Volkan Cevher
The self-concordant-like property of a smooth convex function is a new analytical structure that generalizes the self-concordant notion. While a wide variety of important applicati…
Limits on Support Recovery with Probabilistic Models: An Information-Theoretic Framework
Jonathan Scarlett, Volkan Cevher
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 se…