SPARS 2015

Signal Processing with Adaptive Sparse Structured Representations

July 6-9, 2015, Cambridge, UK.

Poster Sessions

Papers will be grouped into the following topics at the three poster sessions:

Code Topic
A Iterative thresholding
B CS / Nullspace / RIP methods
C Support recovery
D Low-rank matrix methods
E Applications - denoising / deconvolution etc
F not used
G Least squares
H Structured sparsity
I L1 vs Lp norms & reweighting
J Sparse classifiers & machine learning
K Prony methods
L Adaptive sensing
M Dictionary learning

Poster Session 1 - Monday, July 6, 14:30-16:30
Theoretical Aspects of Sparsity

Code Topic
A Iterative thresholding
B CS / Nullspace / RIP methods
C Support recovery
D Low-rank matrix methods
I L1 vs Lp norms & reweighting
K Prony methods

Paper# Topic Title Authors
1013 A Iterative hard thresholding with joint constraints Thomas Blumensath*, University of Southampton
1071 A Local Linear Convergence of Forward--Backward Splitting for Low Complexity Regularization Jingwei Liang*, Jalal Fadili, ENSICAEN; Gabriel Peyré, CNRS & Université Paris-Dauphine, CEREMADE
1088 A Contrast re-enhancement of Total-Variation regularization jointly with the Douglas-Rachford iterations Charles-Alban Deledalle*, Nicolas Papadakis, IMB - CNRS, Univ Bordeaux; Joseph Salmon, CNRS LTCI – Télécom ParisTech
1120 A Local Linear Convergence of an Inertial ISTA Patrick Johnstone*, Pierre Moulin, University of Illinois
1143 A Communication-Efficient Distributed IHT Puxiao Han*, Ruixin Niu, Virginia Commonwealth Univ.; Yonina Eldar, Technion
1036 B Linear embeddings of low-dimensional subsets of a Hilbert space to R^m Gilles Puy*, INRIA; Mike Davies, U Edinburgh; Remi Gribonval, Centre de Recherche INRIA Rennes
1070 B Improved RIP Guarantees for certain Deterministic Matrices based on Coherence Arash Amini*, Sharif University of Tech.
1031 C Heuristic Optimality Checks for Noise-Aware Sparse Recovery by l1-Minimization Christoph Brauer*, Dirk Lorenz, TU Braunschweig; Andreas Tillmann, TU Darmstadt
1032 C A sublinear parallel algorithm for combinatorial compressed sensing Rodrigo Mendoza Smith*, Jared Tanner, University of Oxford
1033 C Phase Transitions in Group Testing Jonathan Scarlett*, Volkan Cevher, EPFL - Lausanne
1058 C Geometric Conditions for Subspace-Sparse Recovery Chong You*, Rene Vidal, The Johns Hopkins University
1072 C Asymptotic of Sparse Support Recovery for Positive Measures Quentin DENOYELLE*, Université Paris-Dauphine; Vincent Duval, INRIA Rocquencourt; Gabriel Peyré, CNRS & Université Paris-Dauphine, CEREMADE
1080 C Time-data tradeoff for the sparse and cosparse regularizations of physics-driven inverse problems Srdan Kitic*, INRIA; Nancy Bertin, IRISA; Remi Gribonval, Centre de Recherche INRIA Rennes
1136 C MRI SNR Improvement Utilizing Sparsity of Difference Between Slices Lior Weizman*, Jonatan Chernyak, Amit Solomon, Yonina Eldar, Moran Artzi, Dafna Ben Bashat, Technion
1087 D Image inpainting using low-rank block Hankel structured matrix from local patches Kyong Hwan Jin, Jong Chul Ye*, Dept. of Bio and Brain Engineering, KAIST
1097 D Online Robust Matrix Completion Brian Lois, Namrata Vaswani*, Iowa State University
1112 D Low-Rank Matrix Recovery from Row&Column Measurements Avishai Wagner*, Or Zuk, Hebrew University of Jerusalem
1145 D Sparse Self-Expressive Decompositions for Dimensionality Reduction and Clustering Eva Dyer*, Rehab. Inst. of Chicago; Raajen Patel, Rich Baraniuk, Rice University; Konrad Kording, Northwestern University; Tom Goldstein, University of Maryland
12 I CEL0: a continuous alternative to l0 penalty Emmanuel Soubies*, Laure Blanc-Féraud, UNS, CNRS I3S; Gilles Aubert, UNS, CNRS JAD
1016 I Expected Patch Log Likelihood with a Sparse Enforcing Prior Jeremias Sulam*, Michael Elad, Technion
1024 I Quadratically fast IRLS for sparse signal recovery Chiara Ravazzi*, Enrico Magli, Politecnico di Torino
1029 I Compressive Gaussian Mixture Estimation by Orthogonal Matching Pursuit with Replacement Nicolas Keriven*, IRISA Rennes; Remi Gribonval, Centre de Recherche INRIA Rennes
1039 I Sparse Gradient Regularisation in Discrete and Continuous Spaces Jan Lellmann*, University of Cambridge; Evgeny Strekalovskiy, Daniel Cremers, TU Munich
1041 I Too Relaxed? Tightly Relaxed Non-convex Sparse Regularization Ankit Parekh*, Ivan Selesnick, New York University
1046 I Markov-tree Bayesian Group-sparse Modeling: Efficient Solutions to Large Inverse Problems Ganchi Zhang*, Nick Kingsbury, University of Cambridge
1061 I The Ordered Weighted l1 Norm: Atomic Formulation and Conditional Gradient Algorithm Mario Figueiredo*, Xiangrong Zeng, IT, IST, University of Lisbon
1073 I Local Linear Convergence of Douglass--Rachford/ADMM for Low Complexity Regularization Jingwei Liang*, Jalal Fadili, ENSICAEN; Gabriel Peyré, CNRS & Université Paris-Dauphine, CEREMADE; Russell Luke, University of Göttingen
1085 I Smoothing Doesn't Improve the Convergence Rate of LASSO Subhadip Mukherjee*, Chandra Sekhar Seelamantula, Indian Institute of Science
1092 I Sparsity for Overparameterized Variational Problems Raja Giryes*, Duke University; Michael Elad, Alfred Bruckstein, Technion
1104 I A Bayesian Method for Sparse Approximation of Functions Michael Tipping*, N/A
1113 I On noise robustness in sparse analysis Marc Nicodème*, IMS; Charles Dossal, IMB; Flavius Turcu, IMS
1131 I Pseudo Marginal MCMC for Parameter Estimation in Alpha Stable Distributions Marina Riabiz*, Fredrik Lindsten, Simon Godsill, University of Cambridge
1110 K Prony’s Method for Multivariate Signals Thomas Peter*, University of Osnabrück, Gerlind Plonka, Robert Schaback, University of Goettingen

Poster Session 2 - Wednesday, July 8, 14:30-16:30
Structured Sparsity and Adaptive Methods

Code Topic
G Least squares
H Structured sparsity
I L1 vs Lp norms & reweighting
J Sparse classifiers & machine learning
L Adaptive sensing
M Dictionary learning

Paper# Topic Title Authors
1018 G Robust Principal Component Analysis in high dimension Ilaria Giulini*, Ecole Normale Superieure
1034 G Non-Negative Orthogonal Least Square: an Efficient Implementation Mehrdad Yaghoobi*, Mike Davies, University of Edinburgh
1067 H Distributed algorithms for in-network recovery of jointly sparse signals Sophie Fosson*, Javier Matamoros, Carles Anton-Haro, Enrico Magli, Politecnico di Torino
1074 H Structural Norm Minimization based on Neighborhoods Roozbeh Manshaei*, Ryerson University; SayedMasoud Hashemi, University of Toronto; Matthew Kyan, Ryerson University
1084 H Structure Aware ADMM Splitting for Discrete Tomography Andreea Denitiu*, Stefania Petra, Heidelberg University; Claudius Schnoerr, Hochschule Munchen; Christoph Schnoerr, University of Heidelberg
1095 H Fast Solving of the Group-Lasso via Dynamic Screening. Antoine Bonnefoy*, Valentin Emiya, liva Ralaivola, Aix-Marseille Université - LIF; Remi Gribonval, Centre de Recherche INRIA Rennes
1096 H Sparsistency of \ell_1-Regularized M-Estimators Yen-Huan Li*, Jonathan Scarlett, Pradeep Ravikumar, Volkan Cevher, EPFL, Lausanne
1108 H Sparse Clustering of Noisy Signals in the Wavelet Domain Tom Hope*, Avishai Wagner, Or Zuk, Hebrew University of Jerusalem
1109 H Structured sparsity: towards a "deep" understanding Angélique Drémeau*, Florent Krzakala, ENSTA Bretagne
1141 H Learning network structure via Hawkes processes Michael Moore*, Mark Davenport, Georgia Institute of Technology
1142 H Anomaly Detection Using Convolutional Sparse Models Diego Carrera*, Giacomo Boracchi, Alessandro Foi, Brendt Wohlberg, Politecnico Di Milano
1051 I Efficient Algorithms for l_infty-Norm Minimization Christoph Studer*, Cornell University; Tom Goldstein, University of Maryland; Wotao Yin, UCLA; Rich Baraniuk, Rice Univ.
1052 I Forward-Backward Splitting Made FASTA Tom Goldstein*, University of Maryland; Christoph Studer, Cornell University; Rich Baraniuk, Rice University
5 J A Semiquantitative Group Testing Approach for Learning Interpretable Clinical Prediction Rules Amin Emad, Kush Varshney*, Dmitry Malioutov, IBM Research;
1037 J On Sufficient Conditions for Affine Sparse Subspace Clustering Chun-Guang Li*, Beijing Univ.of Posts&Telecomm; Chong You, Rene Vidal, The Johns Hopkins University
1044 J Compressed Sensing with Deep Neural Network David Boublil*, Michael Elad, Michael Zibulevsky, Technion;
1053 J Self-Expressive Clustering of Binary Data via Group Sparsity Andrew Lan, Rice University; Christoph Studer*, Cornell University; Rich Baraniuk, Rice University
1090 J Sparse Modeling of Neural Network Posterior Probabilities for Exemplar-Based Speech Recognition Pranay Dighe, Afsaneh Asaei*, Hervé Bourlard, Idiap Research Institute, EPFL
1148 J A Primal-Dual Algorithmic Framework for Constrained Convex Optimization Volkan Cevher*, Quoc Tran Dinh, EPFL, Lausanne
1021 L Sub-Nyquist Volumetric Ultrasound Imaging Tanya Chernyakova*, Yonina Eldar, The Technion, IIT
1063 L Xampling and Frequency Domain Beamforming Application in a Wireless Ultrasound Imaging System Alon Eilam*, Tanya Chernyakova, Samuel Londer, Armand Chocron, Technion; Arcady Kempinski, GE Healthcare; Yonina Eldar, Technion
1126 L Minimum-Time Sampling Using Quantile Search Laura Balzano*, John Lipor, University of Michigan
1149 L Adaptive Dynamical Systems in Compressive Domains as a Manifold Learning Framework Herbert Buchner*, Simon Godsill, University of Cambridge
7 M A Tensor-based Dictionary Approach to Tomographic Image Reconstruction Sara Soltani*, Danmarks Tekniske Universitet; Misha Kilmer, Tufts University
9 M Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment Liansheng Zhuang, Univ. Sci. & Tech. China; Tsung-Han Chan, Adv. Digital Sci. Center Singapore; Allen Yang*, Univ. of California, Berkeley; Yi Ma, ShanghaiTech University
13 M Learning Dictionaries of Orthonormal Bases for Scalable Coding: Application to Biological Images Jonathan Taquet*, Canon Research Centre France; Claude Labit, Centre INRIA Rennes Bretagne Atlantique
1026 M Learning Sparsity Inducing Analysis Operators for Discriminative Similarity Metrics Cagdas Bilen*, Joaquin Zepeda, Patrick Perez, Technicolor
1040 M Regularized Dictionary Learning for Graph Signals Yael Yankelevsky*, Michael Elad, Technion
1045 M On the Sample Complexity of Analysis Operator Learning Matthias Seibert*, Martin Kleinsteuber, Technische Universität München
1050 M Learning a Dynamics Dictionary for Time-Varying Sparse Signals Adam Charles*, Christopher Rozell, Georgia Institute of Technology
1057 M Sparse Regression in Time-Frequency Dictionaries with Non-White Gaussian Noise Matthieu Kowalski*, Univ Paris-Sud; Alexandre Gramfort, Telecom ParisTech
1060 M Reconstruction of Dynamic MRI using a Sparsity Prior on 1D Temporal Snippets Esben Plenge*, Michael Elad, Technion; Mitchell Cooper, Martin Prince, Yi Wang, Pascal Spincemaille, Cornell
1086 M Convolutional Trees for Fast Transform Learning Olivier Chabiron*, Jean-Yves Tourneret, Herwig Wendt, IRIT; François Malgouyres, IMT
1093 M Dictionary Learning for Sparse Representation of Neural Network Exemplars in Speech Recognition Pranay Dighe, Afsaneh Asaei*, Hervé Bourlard, Idiap Research Institute, EPFL
1100 M New Analysis of Multiband Modulated DPSS Dictionaries Zhihui Zhu*, Michael Wakin, Colorado School of Mines
1114 M Cropped Signal Dictionary Without Border Effects Boaz Ophir*, Jeremias Sulam, Michael Zibulevsky, Michael Elad, Technion

Poster Session 3 - Thursday, July 9, 14:50-16:50
Applications of Sparsity

Code Topic
E Applications - denoising / deconvolution etc

Paper# Topic Title Authors
11 E Sparse BSS in the presence of outliers Cecile Chenot*, Jerome Bobin, Jeremy Rapin, CEA
1017 E Disease Classification via Sparse Proteomics Analysis Irena Bojarovska, Technical University of Berlin; Tim Conrad, Nada Cvetkovic, Free University of Berlin; Martin Genzel*, Gitta Kutyniok, TU Berlin; Christof Schütte, Free University of Berlin; Jan Vybiral, Charles University Prague
1019 E Sub-Nyquist Cognitive Radio Shahar Tsiper*, Deborah Cohen, Yonina Eldar, Technion
1030 E Postprocessing of Compressed Images via Sequential Image Denoising Yehuda Dar*, Alfred Bruckstein, Michael Elad, Technion; Raja Giryes, Duke University
1035 E Super-Resolution of SEM Images by Dictionary Learning Shahar Tsiper*, Yonina C. Eldar, Moti Segev, Technion
1038 E A Robust Nonlinear Kalman Smoothing Approach for Dynamic Matrix Factorization Aleksandr Aravkin, Kush Varshney*, Dmitry Malioutov, IBM Research
1049 E Phase-transition behavior in X-ray CT matches that of Gaussian sensing matrices Jakob Joergensen*, Technical University of Denmark; Stefania Petra, Heidelberg University; Emil Sidky, University of Chicago
1064 E An Unified Approach for Over and Under-determined Blind Source Separation Based on Both Sparsity and Decorrelation Fangchen Feng*, Matthieu Kowalski, Univ Paris-Sud
1078 E ALOHA : a link between compressed sensing and parallel MRI Kyong Hwan Jin, Dongwook Lee, Jong Chul Ye*, Dept. of Bio and Brain Engineering, KAIST
1082 E A Fast and Sparsity-Aware Generalization of SMART for Tomographic Particle Image Velocimetry Ioana BARBU, Cédric Herzet*, INRIA
1089 E Image reconstruction from dense binary pixels Or Litany*, Tal Remez, Alex Bronstein, Tel Aviv University
1098 E On compressed sensing in photoacoustic tomography Michael Sandbichler*, Felix Krahmer, Thomas Berer, Peter Burgholzer, Markus Haltmeier, University of Innsbruck
1107 E Compressive Computed Tomography Image Reconstruction with Denoising Message Passing Algorithms Alessandro Perelli*, Mike Davies, University of Edinburgh
1116 E OMP with Unknown Filters for Multipath Channel Estimation Ivan Dokmanic*, Martin Vetterli, EPFL
1118 E A scalable algorithm for radio-interferometric imaging Rafael Carrillo*, Vijay Kartik, Jean-Philippe Thiran, EPFL; Yves Wiaux, Heriot-Watt University
1122 E Discrete solutions to underdetermined linear systems via sparse-based transform Dominique Pastor*, Abdeldjalil Aissa-El-Bey, Telecom Bretagne
1123 E On Compressed Motion Sensing for Tomographical Particle Image Velocimetry Robert Dalitz*, Ecaterina Budnariuc, Stefania Petra, Christoph Schnoerr, University of Heidelberg
1127 E Stable Camera Location Estimation by Convex Programming Onur Ozyesil*, Amit Singer, Princeton University; Ronen Basri, Weizmann Institute of Science
1132 E Single Pixel Fabry Perot Camera: Photoacoustic Image Reconstruction using Data Sparsity Marta Betcke*, Ben Cox, Nam Huynh, Paul Beard, Edward Zhang, Simon Arridge, University College London
1133 E Sparse NMF methods applied to Music Transcription Ken O'Hanlon*, Queen Mary University Of London; Mark Plumbley, CVSSP, U Surrey
1135 E Towards 4D Photoacoustic Tomography Felix Lucka*, Marta Betcke, Simon Arridge, Ben Cox, Nam Huynh, Edward Zhang, Paul Beard, University College London
1137 E Reversible Data Hiding on Compressively Sensed Measurements Mehmet Yamaç*, Boğaziçi University; Çağatay Dikici, Imagination Technologies; Bülent Sankur, Boğaziçi University