1.3k citations · 3.8k across the 222 of their papers we have counts for
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An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation
Jonathan Scarlett, Volkan Cevher
Information theory plays an indispensable role in the development of algorithm-independent impossibility results, both for communication problems and for seemingly distinct areas s…
Near-Optimal Noisy Group Testing via Separate Decoding of Items
Jonathan Scarlett, Volkan Cevher
The group testing problem consists of determining a small set of defective items from a larger set of items based on a number of tests, and is relevant in applications such as medi…
Lower Bounds on Active Learning for Graphical Model Selection
Jonathan Scarlett, Volkan Cevher
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 subs…
Convex block-sparse linear regression with expanders -- provably
Anastasios Kyrillidis, Bubacarr Bah, Rouzbeh Hasheminezhad +3
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…
On the Difficulty of Selecting Ising Models with Approximate Recovery
Jonathan Scarlett, Volkan Cevher
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…
Partial Recovery Bounds for the Sparse Stochastic Block Model
Jonathan Scarlett, Volkan Cevher
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…
Converse Bounds for Noisy Group Testing with Arbitrary Measurement Matrices
Jonathan Scarlett, Volkan Cevher
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 achi…
Learning Data Triage: Linear Decoding Works for Compressive MRI
Yen-Huan Li, Volkan Cevher
The standard approach to compressive sampling considers recovering an unknown deterministic signal with certain known structure, and designing the sub-sampling pattern and recovery…
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…
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…
Convexity in source separation: Models, geometry, and algorithms
Michael B. McCoy, Volkan Cevher, Quoc Tran Dinh +2
Source separation or demixing is the process of extracting multiple components entangled within a signal. Contemporary signal processing presents a host of difficult source separat…
Bilinear Generalized Approximate Message Passing
Jason T. Parker, Philip Schniter, Volkan Cevher
We extend the generalized approximate message passing (G-AMP) approach, originally proposed for high-dimensional generalized-linear regression in the context of compressive sensing…
Fixed Points of Generalized Approximate Message Passing with Arbitrary Matrices
Sundeep Rangan, Philip Schniter, Erwin Riegler +2
The estimation of a random vector with independent components passed through a linear transform followed by a componentwise (possibly nonlinear) output map arises in a range of app…
Sublinear Time, Approximate Model-based Sparse Recovery For All
Anastasios Kyrillidis, Volkan Cevher
We describe a probabilistic, {\it sublinear} runtime, measurement-optimal system for model-based sparse recovery problems through dimensionality reducing, {\em dense} random matric…
Matrix ALPS: Accelerated Low Rank and Sparse Matrix Reconstruction
Anastasios Kyrillidis, Volkan Cevher
We propose Matrix ALPS for recovering a sparse plus low-rank decomposition of a matrix given its corrupted and incomplete linear measurements. Our approach is a first-order project…
Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm
Anastasios Kyrillidis, Volkan Cevher
The least absolute shrinkage and selection operator (LASSO) for linear regression exploits the geometric interplay of the -data error objective and the -norm constr…
Model-Based Compressive Sensing
Richard G. Baraniuk, Volkan Cevher, Marco F. Duarte +1
Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for the acquisition of sparse or compressible signals that can be well approximated by just K << N elements f…