E
Emmanuel J. Candès
Researcher at Stanford University
Publications - 280
Citations - 148481
Emmanuel J. Candès is an academic researcher from Stanford University. The author has contributed to research in topics: Convex optimization & Compressed sensing. The author has an hindex of 102, co-authored 262 publications receiving 135077 citations. Previous affiliations of Emmanuel J. Candès include Samsung & École Normale Supérieure.
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The limits of distribution-free conditional predictive inference
TL;DR: This work aims to explore the space in between exact conditional inference guarantees and what types of relaxations of the conditional coverage property would alleviate some of the practical concerns with marginal coverage guarantees while still being possible to achieve in a distribution-free setting.
Proceedings ArticleDOI
Very high quality image restoration by combining wavelets and curvelets
TL;DR: In this article, the ridgelet and curvelet transforms were applied to the problem of restoring an image from noisy data and compared with those obtained via well established methods based on the thresholding of wavelet coefficients.
Journal ArticleDOI
Controlling the Rate of GWAS False Discoveries
Damian Brzyski,Damian Brzyski,Christine B. Peterson,Piotr Sobczyk,Emmanuel J. Candès,Małgorzata Bogdan,Chiara Sabatti +6 more
TL;DR: This work proposes a novel approach to FDR control that is based on prescreening to identify the level of resolution of distinct hypotheses and shows how FDR-controlling strategies can be adapted to account for this initial selection both with theoretical results and simulations that mimic the dependence structure to be expected in GWAS.
Journal ArticleDOI
A Compressed Sensing Parameter Extraction Platform for Radar Pulse Signal Acquisition
Juhwan Yoo,C. Turnes,E. B. Nakamura,C. K. Le,Stephen Becker,Emilio A. Sovero,Michael B. Wakin,Michael C. Grant,Justin Romberg,Azita Emami-Neyestanak,Emmanuel J. Candès +10 more
TL;DR: A complete (hardware/ software) sub-Nyquist rate (× 13) wideband signal acquisition chain capable of acquiring radar pulse parameters in an instantaneous bandwidth spanning 100 MHz-2.5 GHz with the equivalent of 8 effective number of bits (ENOB) digitizing performance is presented.
Journal ArticleDOI
How well can we estimate a sparse vector
TL;DR: In this paper, the authors established a lower bound on the mean-squared error, which holds regardless of sensing/design matrix being used and regardless of the estimation procedure, and this lower bound very nearly matches the known upper bound one gets by taking a random projection of the sparse vector followed by an l 1 estimation procedure such as the Dantzig selector.