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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Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approach
TL;DR: This article proposed a model-free framework for sensitivity analysis of individual treatment effects (ITEs), building upon ideas from conformal inference, which relies on reliable predictive inference of counterfactuals and ITEs in situations where the training data is confounded.
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Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control.
TL;DR: In this paper, the authors propose a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees regardless of the underlying model and (unknown) data-generating distribution.
Journal ArticleDOI
GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies
Zihuai He,Linxi Liu,Michael E. Belloy,Yann Le Guen,Aaron Sossin,Xiaoxia Liu,Xinran Qi,Shiyang Ma,Prashnna Kumar Gyawali,Tony Wyss-Coray,Hua Tang,Chiara Sabatti,Emmanuel J. Candès,Michael D. Greicius,Iuliana Ionita-Laza +14 more
TL;DR: GhostKnockoff as mentioned in this paper is an efficient knockoff-based method for genome-wide association studies (GWAS) that leads to improved power and ability to prioritize putative causal variants relative to conventional GWAS approaches.
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A Short Tour of Compressive Sensing
TL;DR: In 2013, the Dannie-Heineman-Preis 2013 wurde Emmanuel Jean Candès, Stanford/USA, verliehen as mentioned in this paper, who was einer der Architektendes Compressive Sensing Prinzips die Brücke zwischen Grundlagenforschung and der vielfältigen praktischen Nutzung.
Model-free selective inference under covariate shift via weighted conformal p-values
Ying Jin,Emmanuel J. Candès +1 more
TL;DR: Weighted conformalized selection (WCS) as discussed by the authors is a multiple testing procedure which leverages a special conditional independence structure implied by weighted exchangeability to achieve FDR control in finite samples.