Tractable Evaluation of Stein's Unbiased Risk Estimate With Convex Regularizers

Parth Nobel, Emmanuel J. Candès, Stephen Boyd

Type: Article

Publication Date: 2023-01-01

Citations: 1

DOI: https://doi.org/10.1109/tsp.2023.3323046

View Publication

Locations

IEEE Transactions on Signal Processing - View

Similar Works

Action	Title	Year	Authors
+	Tractable Evaluation of Stein's Unbiased Risk Estimate with Convex Regularizers	2022	Parth Nobel Emmanuel J. Candès Stephen Boyd
+	Unbiased estimates and convex loss functions	1978	Lev B. Klebanov
+	Second order Stein: SURE for SURE and other applications in high-dimensional inference	2018	Pierre Bellec Cun‐Hui Zhang
+	Chapter 3 Mean unbiased estimators and convex loss functions	1994
+ PDF Chat	Statistical Inference via Convex Optimization	2020	Anatoli Juditsky Arkadi Nemirovski
+	Efficient Methods For Large-Scale Empirical Risk Minimization	2017	Aryan Mokhtari
+ PDF Chat	The landscape of empirical risk for nonconvex losses	2018	Mei Song Yu Bai Andrea Montanari
+ PDF Chat	Second-order Stein: SURE for SURE and other applications in high-dimensional inference	2021	Pierre Bellec Cun‐Hui Zhang
+	A scalable modular convex solver for regularized risk minimization	2007	Choon Hui Teo Alex Smola S. V. N. Vishwanathan Quoc V. Le
+	Minimax risk over quadratically convex sets	2010	S. V. Reshetov
+	Existence and determination of optimal estimators relative to convex loss	1965	M. M. Rao
+	Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization	2019	Michael R. Metel Akiko Takeda
+	The Landscape of Empirical Risk for Non-convex Losses	2016	Mei Song Yu Bai Andrea Montanari
+	Variance-based regularization with convex objectives	2016	John C. Duchi Hongseok Namkoong
+	Variance-based Regularization with Convex Objectives	2019	John C. Duchi Hongseok Namkoong
+	Variance-based regularization with convex objectives	2016	John C. Duchi Hongseok Namkoong
+	Variance-based Regularization with Convex Objectives	2017	Hongseok Namkoong John C. Duchi
+	Efficient Nonconvex Empirical Risk Minimization via Adaptive Sample Size Methods	2019	Aryan Mokhtari Asuman Ozdaglar Ali Jadbabaie
+	Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n)	2013	Francis Bach Éric Moulines
+	Convex estimation	2017	Cécile Durot Jade Giguelay Sylvie Huet François Koladjo Stéphane Robin

Works That Cite This (0)

Action	Title	Year	Authors

Works Cited by This (21)

Action	Title	Year	Authors
+ PDF Chat	On the “degrees of freedom” of the lasso	2007	Hui Zou Trevor Hastie Robert Tibshirani
+	Gradient methods for minimizing composite functions	2012	Yu. Nesterov
+ PDF Chat	ON AUTOMATIC DIFFERENTIATION AND ALGORITHMIC LINEARIZATION	2014	Andreas Griewank
+ PDF Chat	Unbiased Risk Estimates for Singular Value Thresholding and Spectral Estimators	2013	Emmanuel J. Candès Carlos Sing‐Long Joshua D. Trzasko
+ PDF Chat	A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging	2010	Antonin Chambolle Thomas Pock
+	An Introduction to the Conjugate Gradient Method Without the Agonizing Pain	1994	Jonathan Richard Shewchuk
+	A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems	2009	Amir Beck Marc Teboulle
+	Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation	2013	Yoshua Bengio Nicholas Léonard Aaron Courville
+	A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression	2013	Noah Simon Jerome H. Friedman Trevor Hastie
+	First-Order Methods in Optimization	2017	Amir Beck