Cover Image

Gradient-based optimization for variational empirical Bayes multiple regression

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Banerjee, Saikat, Carbonetto, Peter, Stephens, Matthew
Format:	Preprint
Published:	2024
Subjects:	Methodology Computation Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression
by: Kim, Youngseok, et al.
Published: (2022)

Improved methods for empirical Bayes multivariate multiple testing and effect size estimation
by: Yang, Yunqi, et al.
Published: (2024)

ebnm: An R Package for Solving the Empirical Bayes Normal Means Problem Using a Variety of Prior Families
by: Willwerscheid, Jason, et al.
Published: (2021)

A New Family of Poisson Non-negative Matrix Factorization Methods Using the Shifted Log Link
by: Weine, Eric, et al.
Published: (2026)

Variational empirical Bayes variable selection in high-dimensional logistic regression
by: Tang, Yiqi, et al.
Published: (2025)

Empirical Bayes Shrinkage of Functional Effects, with Application to Analysis of Dynamic eQTLs
by: Zhang, Ziang, et al.
Published: (2026)

A Flexible Empirical Bayes Approach to Generalized Linear Models, with Applications to Sparse Logistic Regression
by: Xie, Dongyue, et al.
Published: (2026)

Empirical Bayes Covariance Decomposition, and a Solution to the Multiple Tuning Problem in Sparse PCA
by: Kang, Joonsuk, et al.
Published: (2023)

Sparse high-dimensional linear mixed modeling with a partitioned empirical Bayes ECM algorithm
by: Zgodic, Anja, et al.
Published: (2023)

Covariate-moderated Empirical Bayes Matrix Factorization
by: Denault, William R. P., et al.
Published: (2025)

Non-negative matrix factorization algorithms generally improve topic model fits
by: Carbonetto, Peter, et al.
Published: (2021)

Discriminative classification with generative features: bridging Naive Bayes and logistic regression
by: Terner, Zachary, et al.
Published: (2025)

Quasi-Bayes empirical Bayes: a sequential approach to the Poisson compound decision problem
by: Favaro, Stefano, et al.
Published: (2024)

Personalizing black-box models for nonparametric regression with minimax optimality
by: Li, Sai, et al.
Published: (2026)

Approximate Bayesian inference for cumulative probit regression models
by: Aliverti, Emanuele
Published: (2025)

Network-based Neighborhood regression
by: Zhen, Yaoming, et al.
Published: (2024)

Low-rank variational Bayes correction to the Laplace method
by: van Niekerk, Janet, et al.
Published: (2021)

Leveraging heterogeneity for identifiability: Bayesian order-based learning of multiple DAGs
by: Chang, Hyunwoong, et al.
Published: (2026)

Deep Bayes Factors
by: Kim, Jungeum, et al.
Published: (2023)

The MELODIC family for simultaneous binary logistic regression in a reduced space
by: de Rooij, Mark, et al.
Published: (2021)

High-dimensional reliability-based design optimization using stochastic emulators
by: Moustapha, M., et al.
Published: (2026)

Mixing times of data-augmentation Gibbs samplers for high-dimensional probit regression
by: Ascolani, Filippo, et al.
Published: (2025)

Deep Generative Quantile Bayes
by: Kim, Jungeum, et al.
Published: (2024)

Tree Bandits for Generative Bayes
by: O'Hagan, Sean, et al.
Published: (2024)

On metric choice in dimension reduction for Fréchet regression
by: Soale, Abdul-Nasah, et al.
Published: (2024)

Bayesian Federated Inference for regression models based on non-shared multicenter data sets from heterogeneous populations
by: Jonker, Marianne A, et al.
Published: (2024)

A self-supervised learning approach for denoising autoregressive models with additive noise: finite and infinite variance cases
by: Banerjee, Sayantan, et al.
Published: (2025)

Empirical Bayes Estimation with Side Information: A Nonparametric Integrative Tweedie Approach
by: Luo, Jiajun, et al.
Published: (2023)

Fast Variational Bayes for Large Spatial Data
by: Song, Jiafang, et al.
Published: (2025)

Predictive variational inference: Learn the predictively optimal posterior distribution
by: Lai, Jinlin, et al.
Published: (2024)

Mean-field Variational Bayes for Sparse Probit Regression
by: Fasano, Augusto, et al.
Published: (2026)

Partially factorized variational inference for high-dimensional mixed models
by: Goplerud, Max, et al.
Published: (2023)

Bayesian Ensembling: Insights from Online Optimization and Empirical Bayes
by: Waxman, Daniel, et al.
Published: (2025)

Inversion-Free Natural Gradient Descent on Riemannian Manifolds
by: Draca, Dario, et al.
Published: (2026)

Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based Sampling
by: Ichikawa, Yuma, et al.
Published: (2024)

Pathwise Gradient Variance Reduction with Control Variates in Variational Inference
by: Ng, Kenyon, et al.
Published: (2024)

Wasserstein Gradient Flows for Batch Bayesian Optimal Experimental Design
by: Sharrock, Louis
Published: (2026)

Monte Carlo inference for semiparametric Bayesian regression
by: Kowal, Daniel R., et al.
Published: (2023)

Global convergence of optimized adaptive importance samplers
by: Akyildiz, Ömer Deniz
Published: (2022)

Barycentric model aggregation in the Wasserstein space of distributions and a variational approach to consistency
by: Androulakis, Emmanouil, et al.
Published: (2025)