scispace - formally typeset
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.

Papers
More filters
Posted Content

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

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

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.