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1,331
Nearoptimal differentially private principal components
 In Proc. 26th Annual Conference on Neural Information Processing Systems (NIPS
"... Principal components analysis (PCA) is a standard tool for identifying good lowdimensional approximations to data sets in high dimension. Many current data sets of interest contain private or sensitive information about individuals. Algorithms which operate on such data should be sensitive to the pr ..."
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Cited by 12 (1 self)
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Principal components analysis (PCA) is a standard tool for identifying good lowdimensional approximations to data sets in high dimension. Many current data sets of interest contain private or sensitive information about individuals. Algorithms which operate on such data should be sensitive
A NearOptimal Algorithm for DifferentiallyPrivate Principal Components
"... The principal components analysis (PCA) algorithm is a standard tool for identifying good lowdimensional approximations to highdimensional data. Many data sets of interest contain private or sensitive information about individuals. Algorithms which operate on such data should be sensitive to the p ..."
Abstract

Cited by 8 (2 self)
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The principal components analysis (PCA) algorithm is a standard tool for identifying good lowdimensional approximations to highdimensional data. Many data sets of interest contain private or sensitive information about individuals. Algorithms which operate on such data should be sensitive
On the distribution of the largest eigenvalue in principal components analysis
 ANN. STATIST
, 2001
"... Let x �1 � denote the square of the largest singular value of an n × p matrix X, all of whose entries are independent standard Gaussian variates. Equivalently, x �1 � is the largest principal component variance of the covariance matrix X ′ X, or the largest eigenvalue of a pvariate Wishart distribu ..."
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Cited by 422 (4 self)
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Let x �1 � denote the square of the largest singular value of an n × p matrix X, all of whose entries are independent standard Gaussian variates. Equivalently, x �1 � is the largest principal component variance of the covariance matrix X ′ X, or the largest eigenvalue of a pvariate Wishart
Principal Components Analysis to Summarize Microarray Experiments: Application to Sporulation Time Series
 in Pacific Symposium on Biocomputing
, 2000
"... A series of microarray experiments produces observations of differential expression for thousands of genes across multiple conditions. It is often not clear whether a set of experiments are measuring fundamentally different gene expression states or are measuring similar states created through diffe ..."
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Cited by 202 (2 self)
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different mechanisms. It is useful, therefore, to define a core set of independent features for the expression states that allow them to be compared directly. Principal components analysis (PCA) is a statistical technique for determining the key variables in a multidimensional data set that explain
Population structure and eigenanalysis
 PLoS Genet 2(12): e190 DOI: 10.1371/journal.pgen.0020190
, 2006
"... Current methods for inferring population structure from genetic data do not provide formal significance tests for population differentiation. We discuss an approach to studying population structure (principal components analysis) that was first applied to genetic data by CavalliSforza and colleague ..."
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Cited by 263 (9 self)
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Current methods for inferring population structure from genetic data do not provide formal significance tests for population differentiation. We discuss an approach to studying population structure (principal components analysis) that was first applied to genetic data by Cavalli
Constructing SocioEconomic Status Indices: How to Use Principal Components Analysis,
 in association with The London School of Hygiene and Tropical Medicine,
, 2006
"... Theoretically, measures of household wealth can be reflected by income, consumption or expenditure information. However, the collection of accurate income and consumption data requires extensive resources for household surveys. Given the increasingly routine application of principal components anal ..."
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Cited by 164 (0 self)
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Theoretically, measures of household wealth can be reflected by income, consumption or expenditure information. However, the collection of accurate income and consumption data requires extensive resources for household surveys. Given the increasingly routine application of principal components
NearOptimal MAP Inference for Determinantal Point Processes
"... Determinantal point processes (DPPs) have recently been proposed as computationally efficient probabilistic models of diverse sets for a variety of applications, including document summarization, image search, and pose estimation. Many DPP inference operations, including normalization and sampling, ..."
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Cited by 14 (3 self)
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, are tractable; however, finding the most likely configuration (MAP), which is often required in practice for decoding, is NPhard, so we must resort to approximate inference. This optimization problem, which also arises in experimental design and sensor placement, involves finding the largest principal minor
Toxic Cyanobacteria in Water: A guide to their public health consequences, monitoring and management
, 1999
"... Identification and quantification of cyanobacteria in water resources is the principal component of cyanotoxin monitoring programmes and can provide an effective early warning system for the development of potentially toxic blooms. Data on concentrations of total phosphate, nitrate and ammonia are v ..."
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Cited by 178 (2 self)
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Identification and quantification of cyanobacteria in water resources is the principal component of cyanotoxin monitoring programmes and can provide an effective early warning system for the development of potentially toxic blooms. Data on concentrations of total phosphate, nitrate and ammonia
Behavioral theories and the neurophysiology of reward,
 Annu. Rev. Psychol.
, 2006
"... ■ Abstract The functions of rewards are based primarily on their effects on behavior and are less directly governed by the physics and chemistry of input events as in sensory systems. Therefore, the investigation of neural mechanisms underlying reward functions requires behavioral theories that can ..."
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Cited by 187 (0 self)
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you quite quickly what to choose. However, you cannot use the same simple emotional judgment when you are in the shoes of an economist trying to optimize the access to the locks on the Mississippi River. The task is to find a pricing structure that assures the most efficient and uninterrupted use
Nearly Optimal Private Convolution
"... Abstract. We study algorithms for computing the convolution of a private input x with a public input h, while satisfying the guarantees of (ε, δ)differential privacy. Convolution is a fundamental operation, intimately related to Fourier Transforms. In our setting, the private input may represent ..."
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an algorithm for computing convolutions which satisfies (ε, δ)differentially privacy and is nearly optimal for every public h, i.e. is instance optimal with respect to the public input. We prove optimality via spectral lower bounds on the hereditary discrepancy of convolution matrices. Our algorithm is very
Results 1  10
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1,331