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The Johnson-Lindenstrauss property ({\sf JLP}) of random matrices has immense application in computer science ranging from compressed sensing, learning theory, numerical linear algebra, to privacy.
Some results relating moment generating functions and convergence rates in the law of large numbers
David Lee Hanson · 1967
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A bound on tail probabilities for quadratic forms in independent random variables
David Lee Hanson and Farroll Tim Wright · 1971
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Probability inequalities for the sum in sampling without replacement
Robert J Serfling · 1974
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Martingales with values in uniformly convex spaces
Gilles Pisier · 1975
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Extensions of Lipschitz mappings into a Hilbert space
William B Johnson and Joram Lindenstrauss · 1984
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Uniform convexity properties of norms on a super-reflexive banach space
Catherine Finet · 1986
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More on embedding subspaces of L p L_{p} in ℓ r n \ell^{n}_{r}
Gideon Schechtman · 1987
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A course in functional analysis
John B Conway · 1990
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Approximate nearest neighbors: Towards removing the curse of dimensionality
Piotr Indyk and Rajeev Motwani · 1998
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An algorithmic theory of learning: Robust concepts and random projection
Rosa I Arriaga and Santosh Vempala · 1999
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Decoupling: from dependence to independence
Victor De la Pena and Evarist Giné · 1999
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Graph approximations to geodesics on embedded manifolds
Mira Bernstein, Vin De Silva, John C Langford, and Joshua B Tenenbaum · 2000
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A global geometric framework for nonlinear dimensionality reduction
Joshua B Tenenbaum, Vin De Silva, and John C Langford · 2000
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Dimension Reduction in the ℓ 1 \ell_{1} Norm
Moses Charikar and Amit Sahai · 2002
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Database-friendly random projections: Johnson-Lindenstrauss with binary coins
Dimitris Achlioptas · 2003
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An elementary proof of a theorem of Johnson and Lindenstrauss
Sanjoy Dasgupta and Anupam Gupta · 2003
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Consequences and Limits of Nonlocal Strategies
Richard Cleve, Peter Høyer, Benjamin Toner, and John Watrous · 2004
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Measuring the strangeness of strange attractors
Peter Grassberger and Itamar Procaccia · 2004
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Kernels as features: On kernels, margins, and low-dimensional mappings
Maria-Florina Balcan, Avrim Blum, and Santosh Vempala · 2006
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Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
Emmanuel J. Candès, Justin K. Romberg, and Terence Tao · 2006
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Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Emmanuel J. Candès and Terence Tao · 2006
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Compressed sensing
David L. Donoho · 2006
Cited alongside, same era.
Our Data, Ourselves: Privacy Via Distributed Noise Generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
Cited alongside, same era.
Calibrating Noise to Sensitivity in Private Data Analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Cited alongside, same era.
Improved approximation algorithms for large matrices via random projections
Tamas Sarlos · 2006
Cited alongside, same era.
The generic chaining: upper and lower bounds of stochastic processes
Michel Talagrand · 2006
Cited alongside, same era.
Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
Cited alongside, same era.
The Johnson-Lindenstrauss Transform Itself Preserves Differential Privacy
Jeremiah Blocki, Avrim Blum, Anupam Datta, and Or Sheffet · 2012
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Iterative constructions and private data release
Anupam Gupta, Aaron Roth, and Jonathan Ullman · 2012
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Beating randomized response on incoherent matrices
Moritz Hardt and Aaron Roth · 2012
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On significance of the least significant bits for differential privacy
Ilya Mironov · 2012
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An Almost Optimal Unrestricted Fast Johnson-Lindenstrauss Transform
Nir Ailon and Edo Liberty · 2013
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Differentially private data analysis of social networks via restricted sensitivity
Jeremiah Blocki, Avrim Blum, Anupam Datta, and Or Sheffet · 2013
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A Simple Proof of the Restricted Isometry Property for Random Matrices
Richard Baraniuk, Mark Davenport, Ronald DeVore, and Michael Wakin · 2008
Cited alongside, same era.
Random projections for manifold learning
Chinmay Hegde, Michael Wakin, and Richard Baraniuk · 2008
Cited alongside, same era.
On variants of the Johnson-Lindenstrauss lemma
Jirí Matousek · 2008
Cited alongside, same era.
On sparse reconstruction from Fourier and Gaussian measurements
Mark Rudelson and Roman Vershynin · 2008
Cited alongside, same era.
The Fast Johnson–Lindenstrauss Transform and Approximate Nearest Neighbors
Nir Ailon and Bernard Chazelle · 2009
Cited alongside, same era.
Fast Dimension Reduction Using Rademacher Series on Dual BCH Codes
Nir Ailon and Edo Liberty · 2009
Cited alongside, same era.
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A learning theory approach to noninteractive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2013
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Restricted isometry of fourier matrices and list decodability of random linear codes
Mahdi Cheraghchi, Venkatesan Guruswami, and Ameya Velingker · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2013
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Beyond worst-case analysis in private singular vector computation
Moritz Hardt and Aaron Roth · 2013
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Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression
Xiangrui Meng and Michael W Mahoney · 2013
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Osnap: Faster numerical linear algebra algorithms via sparser subspace embeddings
Jelani Nelson and Huy L Nguyên · 2013
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Hanson-wright inequality and sub-gaussian concentration
Mark Rudelson and Roman Vershynin · 2013
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Random Projections, Graph Sparsification, and Differential Privacy
Jalaj Upadhyay · 2013
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Fast and RIP-optimal transforms
Nir Ailon and Holger Rauhut · 2014
Closest in time.
The Restricted Isometry Property for the General p-Norms
Zeyuan Allen-Zhu, Rati Gelashvili, and Ilya Razenshteyn · 2014
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Sparser Johnson-Lindenstrauss Transforms
Daniel M. Kane and Jelani Nelson · 2014
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Suprema of chaos processes and the restricted isometry property
Felix Krahmer, Shahar Mendelson, and Holger Rauhut · 2014
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New constructions of rip matrices with fast multiplication and fewer rows
Jelani Nelson, Eric Price, and Mary Wootters · 2014
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Differentially private linear algebra in the streaming model
Jalaj Upadhyay · 2014
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