Balls and bins: a study in negative dependence
Random Structures & Algorithms
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
The phase transition in a random hypergraph
Journal of Computational and Applied Mathematics - Special issue: Probabilistic methods in combinatorics and combinatorial optimization
Data streams: algorithms and applications
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Condition Numbers of Gaussian Random Matrices
SIAM Journal on Matrix Analysis and Applications
Counting connected graphs and hypergraphs via the probabilistic method
Random Structures & Algorithms
Block-sparsity: Coherence and efficient recovery
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
On the reconstruction of block-sparse signals with an optimal number of measurements
IEEE Transactions on Signal Processing
Compressive Sensing by Random Convolution
SIAM Journal on Imaging Sciences
Model-based compressive sensing
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
K-median clustering, model-based compressive sensing, and sparse recovery for earth mover distance
Proceedings of the forty-third annual ACM symposium on Theory of computing
Sparse recovery with partial support knowledge
APPROX'11/RANDOM'11 Proceedings of the 14th international workshop and 15th international conference on Approximation, randomization, and combinatorial optimization: algorithms and techniques
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We develop an algorithm for estimating the values of a vector x ε Rn over a support S of size k from a randomized sparse binary linear sketch Ax of size O(k). Given Ax and S, we can recover x' with ||x' -- xS||2 ≤ ε || x -- xS||2 with probability at least 1 − k−Ω(1). The recovery takes O(k) time. While interesting in its own right, this primitive also has a number of applications. For example, we can: 1. Improve the linear k-sparse recovery of heavy hitters in Zipfian distributions with O(k log n) space from a 1 + ε approximation to a 1 + o(1) approximation, giving the first such approximation in O(k log n) space when k ≤ O(n1-ε). 2. Recover block-sparse vectors with O(k) space and a 1 +ε approximation. Previous algorithms required either ω(k) space or ω(1) approximation.