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 |