Reading Seminar on Compressive Sensing,
Extensions, and Applications
Schedule
During Fall 2015, the seminar meets on Thursdays at 4pm in Blocker 628.
1 Oct: Overview of the Mathematics of Compressive Sensing - Part 1, presented by Simon Foucart.
(slides)
8 Oct: Overview of the Mathematics of Compressive Sensing - Part 2, presented by Simon Foucart.
15 Oct: Overview of the Mathematics of Compressive Sensing - Part 3, presented by Simon Foucart.
22 Oct: Overview of the Mathematics of Compressive Sensing - Part 4, presented by Simon Foucart.
Paper Bank
Any appropriate suggestion from the participants, as well as
A. Aldroubi, C. Cabrelli, U. Molter, and S. Tang. Dynamical sampling.
(arXiv)
D. Amelunxen, M. Lotz, M. McCoy, and J. Tropp.
Living on the edge: phase transitions in convex programs with random data
(arXiv)
E. Arias-Castro, E. Candès, and M. Davenport. On the fundamental limits of adaptive sensing.
(arXiv)
K. Audenaert. A generalisation of Mirsky's singular value inequalities.
(arXiv)
A. Bandeira, D. Mixon, and J. Moreira. A conditional construction of restricted isometries.
(arXiv)
T. Bendory, S. Dekel, and A. Feuer: Exact recovery of Dirac ensembles from the projection onto spaces of spherical harmonics.
(arXiv)
J. Blanchard and J. Tanner. GPU accelerated greedy algorithms for compressed sensing.
(doi)
J. Bourgain. An improved estimate in the restricted isometry problem.
(doi)
E. Candès. Mathematics of sparsity (and a few other things).
(link)
Y. Chang, J. Gray, and C. Tomlin. Exact reconstruction of gene regulatory networks using compressive sensing.
(link)
J. Chiu and L. Demanet, Matrix probing and its conditioning.
(arXiv)
A. Cohen, M. Davenport, and D. Leviatan. On the stability and accuracy of least squares approximations.
(arXiv)
J. Dick, F. Kuo, and I. Sloan. High-dimensional integration - the quasi-Monte Carlo way.
(doi)
M. Figueiredo and R. Nowak. Sparse Estimation with Strongly Correlated Variables using Ordered Weighted L1 Regularization.
(arXiv)
S. Friedland, Q. Li, and D. Schonfeld. Compressive Sensing of Sparse Tensors.
(arXiv)
L. Gao, J. Liang, C. Li, and L. Wang. Single-shot compressed ultrafast photography at one hundred billion frames per second.
(doi)
M. Gavish and D. Donoho. The optimal hard threshold for singular values is 4/sqrt(3).
(arXiv)
C. Hedge, P. Indyk, and L. Schmidt. Approximation algorithms for model-based compressive sensing.
(arXiv)
M. Iwen, A. Viswanathan, and Y. Wang. Robust sparse phase retrieval made easy.
(arXiv)
J.-P. Kahane. Variantes sur un théorème de Candès, Romberg et Tao.
(arXiv)
G. Lecué and S. Mendelson. Sparse recovery under weak moment assumptions.
(arXiv)
R. Mendoza-Smith and J. Tanner. Expander l0-decoding.
(arXiv)
Y. Plan and R. Vershynin. Robust 1-bit compressed sensing and sparse logistic regression: A convex programming approach.
(arXiv)
Y. Plan and R. Vershynin. The generalized Lasso with non-linear observations.
(arXiv)
E. Ryu and S. Boyd. Extensions of Gauss Quadrature Via Linear Programming.
(doi)
I. Sloan and H. Woźniakowski. When are Quasi-Monte Carlo algorithms efficient for high dimensional integrals?
(doi)
Y. Soh and V. Chandrasekaran. High-dimensional change-point estimation: combining filtering with convex optimization.
(arXiv)
G. Tang, B. Bhaskar, P. Shah, and B. Recht. Compressed sensing off the grid.
(arXiv)
A. Tillmann and M. Pfetsch. The computational complexity of the restricted isometry property, the nullspace property, and related concepts in Compressed Sensing.
(arXiv)
J. Tropp. Convex recovery of a structured signal from independent random linear measurements.
(arXiv)
J. Tropp. User-friendly tail bounds for sums of random matrices.
(arXiv)
R. Vershynin. Estimation in high dimensions: a geometric perspective.
(arXiv)
I. Waldspurger, A. d'Aspremont, and S. Mallat. Phase recovery, MaxCut and complex semidefinite programming.
(arXiv)
F. Zhou, W. Nielson, Y. Xia, and V. Ozolins. Lattice anharmonicity and thermal conductivity from compressive sensing of first-principles calculations. (doi)