1 code implementation • 31 Jul 2023 • Anastasios N. Angelopoulos, Emmanuel J. Candes, Ryan J. Tibshirani
We study the problem of uncertainty quantification for time series prediction, with the goal of providing easy-to-use algorithms with formal guarantees.
no code implementations • 8 May 2019 • Rina Foygel Barber, Emmanuel J. Candes, Aaditya Ramdas, Ryan J. Tibshirani
This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals.
Methodology
1 code implementation • NeurIPS 2019 • Rina Foygel Barber, Emmanuel J. Candes, Aaditya Ramdas, Ryan J. Tibshirani
We extend conformal prediction methodology beyond the case of exchangeable data.
Methodology
no code implementations • 25 Apr 2018 • Emmanuel J. Candes, Pragya Sur
This paper rigorously establishes that the existence of the maximum likelihood estimate (MLE) in high-dimensional logistic regression models with Gaussian covariates undergoes a sharp `phase transition'.
no code implementations • NeurIPS 2015 • Yuxin Chen, Emmanuel J. Candes
We complement our theoretical study with numerical examples showing that solving random quadratic systems is both computationally and statistically not much harder than solving linear systems of the same size---hence the title of this paper.
no code implementations • 4 Mar 2015 • Weijie Su, Stephen Boyd, Emmanuel J. Candes
We derive a second-order ordinary differential equation (ODE) which is the limit of Nesterov's accelerated gradient method.
3 code implementations • 18 Dec 2009 • Emmanuel J. Candes, Xiao-Dong Li, Yi Ma, John Wright
This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted.
Information Theory Information Theory
4 code implementations • 18 Oct 2008 • Jian-Feng Cai, Emmanuel J. Candes, Zuowei Shen
Off-the-shelf algorithms such as interior point methods are not directly amenable to large problems of this kind with over a million unknown entries.
Optimization and Control
no code implementations • 29 May 2008 • Emmanuel J. Candes, Benjamin Recht
We show that one can perfectly recover most low-rank matrices from what appears to be an incomplete set of entries.
Information Theory Information Theory