Publications related to Compressed Sensing of Memoryless Sources

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

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

Polarization of the Renyi Information Dimension With Applications to Compressed Sensing

Emmanuel Abbé, Saeid Haghighatshoar

In this paper, we show that the Hadamard matrix acts as an extractor over the reals of the Renyi Information Dimension (RID), in an analogous way to how it acts as an extractor of the discrete entropy over finite fields. More precisely, we prove that the R ...

Ieee-Inst Electrical Electronics Engineers Inc2017

Visualization, Adaptation, and Transformation of Procedural Grammars

Stefan Lienhard

Procedural shape grammars are powerful tools for the automatic generation of highly detailed 3D content from a set of descriptive rules. It is easy to encode variations in stochastic and parametric grammars, and an uncountable number of models can be gener ...

EPFL2017

Learning Search Strategies from Human Demonstrations

Guillaume Pierre Luc De Chambrier

Decision making and planning with partial state information is a problem faced by all forms of intelligent entities. The formulation of a problem under partial state information leads to an exorbitant set of choices with associated probabilistic outcomes m ...

EPFL2016

Dimensionality Reduction in Dynamic Optimization under Uncertainty

Napat Rujeerapaiboon

Dynamic optimization problems affected by uncertainty are ubiquitous in many application domains. Decision makers typically model the uncertainty through random variables governed by a probability distribution. If the distribution is precisely known, then ...

EPFL2016

A Comment on “Computational Complexity of Stochastic Programming Problems”

Daniel Kuhn, Grani Adiwena Hanasusanto, Wolfram Wiesemann

Although stochastic programming problems were always believed to be computationally challenging, this perception has only recently received a theoretical justification by the seminal work of Dyer and Stougie (Mathematical Programming A, 106(3):423–432, 200 ...

Springer Heidelberg2016

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

Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices

Volkan Cevher, Jonathan Mark Scarlett

Linear sketching is a powerful tool for the problem of sparse signal recovery, having numerous applications such as compressive sensing, data stream computing, graph sketching, and routing. Motivated by applications where the \emph{positions} of the non-ze ...

2016

Learning-Based Compressive Subsampling

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

The problem of recovering a structured signal

\mathbf{x} \in \mathbb{C}^p

from a set of dimensionality-reduced linear measurements

\mathbf{b} = \mathbf {A}\mathbf {x}

arises in a variety of applications, such as medical imaging, spectroscopy, Fourier o ...

Ieee-Inst Electrical Electronics Engineers Inc2016

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

We consider design of sparse controllers for a stochastic linear system with infinite horizon quadratic objective. We formulate the non-sparse optimal solution through a semidefinite program for the second order moments of the states and inputs. Given that ...

University of Minnesota2016

Compressed Sensing of Memoryless Sources

Graph Chatbot

Chat with Graph Search

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

Polarization of the Renyi Information Dimension With Applications to Compressed Sensing

Visualization, Adaptation, and Transformation of Procedural Grammars

Learning Search Strategies from Human Demonstrations

Dimensionality Reduction in Dynamic Optimization under Uncertainty

A Comment on “Computational Complexity of Stochastic Programming Problems”

Convex block-sparse linear regression with expanders - provably

Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices

Learning-Based Compressive Subsampling

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

Dimensionality Reduction in Dynamic Optimization under Uncertainty

Polarization of the Renyi Information Dimension With Applications to Compressed Sensing

A Comment on “Computational Complexity of Stochastic Programming Problems”

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

Convex block-sparse linear regression with expanders - provably

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices

Visualization, Adaptation, and Transformation of Procedural Grammars

Learning Search Strategies from Human Demonstrations

Learning-Based Compressive Subsampling