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Paper Citation Record · LEDGER

Gradient-based optimization for variational empirical Bayes multiple regression

As of 13 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2411.14570.

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pith.paper-citation-record.v1
2411.14570 v1

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measured 49 of 49 reference resolution

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Reference resolution

49 of 49 outbound references displayed

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External citation measurements

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Outbound references

Observation 685d8eb6-ff5e-4241-8619-77e1325187f6 · outbound

This paper cites Studies in the history of probability and statistics XL Boscovich, Simpson and a 1760 manuscript note on fitting a linear relation.

Gradient-based optimization for variational empirical Bayes multiple regression Studies in the history of probability and statistics XL Boscovich, Simpson and a 1760 manuscript note on fitting a linear relation

Reference 1

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Gradient-based optimization for variational empirical Bayes multiple regression Statistical challenges of high-dimensional data

Reference 2

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Gradient-based optimization for variational empirical Bayes multiple regression Statistical learning with sparsity

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Gradient-based optimization for variational empirical Bayes multiple regression ℓ1 Trend Filtering

Reference 4

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Gradient-based optimization for variational empirical Bayes multiple regression Adaptive piecewise polynomial estimation via trend filtering

Reference 5

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Gradient-based optimization for variational empirical Bayes multiple regression Unresolved cited work

Reference 6

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Gradient-based optimization for variational empirical Bayes multiple regression V on Storch and F

Reference 7

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This paper cites High-Dimensional Statistics with a View Toward Applications in Biology.

Gradient-based optimization for variational empirical Bayes multiple regression High-Dimensional Statistics with a View Toward Applications in Biology

Reference 8

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Gradient-based optimization for variational empirical Bayes multiple regression Unresolved cited work

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Gradient-based optimization for variational empirical Bayes multiple regression Economic Predictions With Big Data: The Illusion of Sparsity

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This paper cites Bayesian Variable Selection Regression for Genome-wide Association Studies and Other Large-Scale Problems.

Gradient-based optimization for variational empirical Bayes multiple regression Bayesian Variable Selection Regression for Genome-wide Association Studies and Other Large-Scale Problems

Reference 11

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This paper cites Scalable Variational Inference for Bayesian Variable Selection in Regression, and Its Accuracy in Genetic Association Studies.

Gradient-based optimization for variational empirical Bayes multiple regression Scalable Variational Inference for Bayesian Variable Selection in Regression, and Its Accuracy in Genetic Association Studies

Reference 12

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Gradient-based optimization for variational empirical Bayes multiple regression A Simple New Approach to Variable Selection in Regression, with Application to Genetic Fine Mapping

Reference 13

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Gradient-based optimization for variational empirical Bayes multiple regression Polygenic Modeling with Bayesian Sparse Linear Mixed Models

Reference 14

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Gradient-based optimization for variational empirical Bayes multiple regression A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with appli- cation to the UK Biobank

Reference 15

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Gradient-based optimization for variational empirical Bayes multiple regression Unresolved cited work

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Gradient-based optimization for variational empirical Bayes multiple regression Gene selection in cancer classification using sparse logistic regression with Bayesian regularization

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Gradient-based optimization for variational empirical Bayes multiple regression On Time Series Analysis of Public Health and Biomedical Data

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Gradient-based optimization for variational empirical Bayes multiple regression Building Better Models: Prediction, Replication, and Machine Learning in the Social Sciences

Reference 19

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Gradient-based optimization for variational empirical Bayes multiple regression Text Regression Analysis: A Review, Empirical, and Experimental Insights

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Gradient-based optimization for variational empirical Bayes multiple regression Ridge Regression: Biased Estimation for Nonorthogonal Problems

Reference 21

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Gradient-based optimization for variational empirical Bayes multiple regression Regression Shrinkage and Selection via the Lasso

Reference 22

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Gradient-based optimization for variational empirical Bayes multiple regression Regularization and Variable Selection Via the Elastic Net

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Gradient-based optimization for variational empirical Bayes multiple regression Bayesian Variable Selection in Linear Regression

Reference 24

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Gradient-based optimization for variational empirical Bayes multiple regression Variable Selection via Gibbs Sampling

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Gradient-based optimization for variational empirical Bayes multiple regression The Bayesian Lasso

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Gradient-based optimization for variational empirical Bayes multiple regression The Bayesian elastic net

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Gradient-based optimization for variational empirical Bayes multiple regression False discovery rates: a new deal

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Gradient-based optimization for variational empirical Bayes multiple regression A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression

Reference 29

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Gradient-based optimization for variational empirical Bayes multiple regression Regularization Paths for Generalized Linear Models via Coordinate Descent

Reference 30

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Gradient-based optimization for variational empirical Bayes multiple regression Ridge Estimators in Logistic Regression

Reference 31

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Gradient-based optimization for variational empirical Bayes multiple regression An algorithm for quadratic programming

Reference 32

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Gradient-based optimization for variational empirical Bayes multiple regression Solving large scale linear prediction problems using stochastic gradient descent algorithms

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Gradient-based optimization for variational empirical Bayes multiple regression Nocedal and S

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Gradient-based optimization for variational empirical Bayes multiple regression Variable Metric Method for Minimization

Reference 35

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Gradient-based optimization for variational empirical Bayes multiple regression Variable Metric Method for Minimization

Reference 36

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Gradient-based optimization for variational empirical Bayes multiple regression Unresolved cited work

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This paper cites On information and sufficiency.

Gradient-based optimization for variational empirical Bayes multiple regression On information and sufficiency

Reference 38

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This paper cites An Introduction to Variational Methods for Graphical Models.

Gradient-based optimization for variational empirical Bayes multiple regression An Introduction to Variational Methods for Graphical Models

Reference 39

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This paper cites Variational Inference: A Review for Statisticians.

Gradient-based optimization for variational empirical Bayes multiple regression Variational Inference: A Review for Statisticians

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This paper cites Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, vol. 1: Contributions to the Theory of Statistics.

Gradient-based optimization for variational empirical Bayes multiple regression Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, vol. 1: Contributions to the Theory of Statistics

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This paper cites Trisection algorithms.

Gradient-based optimization for variational empirical Bayes multiple regression Trisection algorithms

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This paper cites Fast Switch and Spline Function Inversion Algorithm with Multistep Optimiza- tion and k-Vector Search for Solving Kepler’s Equation in Celestial Mechanics.

Gradient-based optimization for variational empirical Bayes multiple regression Fast Switch and Spline Function Inversion Algorithm with Multistep Optimiza- tion and k-Vector Search for Solving Kepler’s Equation in Celestial Mechanics

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Observation 7c5c6496-37cd-47fc-89b7-c1de1a9dfeea · outbound

This paper cites On the limited memory BFGS method for large scale optimization.

Gradient-based optimization for variational empirical Bayes multiple regression On the limited memory BFGS method for large scale optimization

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This paper cites A Limited Memory Algorithm for Bound Constrained Optimization.

Gradient-based optimization for variational empirical Bayes multiple regression A Limited Memory Algorithm for Bound Constrained Optimization

Reference 45

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This paper cites Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization.

Gradient-based optimization for variational empirical Bayes multiple regression Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization

Reference 46

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Observation 6608fdf9-b2ae-4598-a7bb-ee0422ac6aeb · outbound

This paper cites Stochastic Variational Inference.

Gradient-based optimization for variational empirical Bayes multiple regression Stochastic Variational Inference

Reference 47

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Observation 2d539d41-70f4-4393-b0ae-f265984f317a · outbound

This paper cites Black Box Variational Inference.

Gradient-based optimization for variational empirical Bayes multiple regression Black Box Variational Inference

Reference 48

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Observation 8bcc736d-02e2-49c2-b3e4-73a2a5212ee7 · outbound

This paper cites Automatic Differentiation Variational Inference.

Gradient-based optimization for variational empirical Bayes multiple regression Automatic Differentiation Variational Inference

Reference 49

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