arXiv research

Empirical Bayes rates via variational approximations and prior decomposition.

Bayesian Empirical Bayes extends EB to complex structures using probabilistic symmetry.

Neural Empirical Bayes estimates source distributions from noisy simulations.

In this study, we consider an empirical Bayes method for Boltzmann machines and propose an algorithm for it. The empirical Bayes method allows estimation of the values of the hyperparameters of the Boltzmann machine by maximizing a specific likelihood function referred to as the empirical Bayes likelihood function in t…

Develops a fast variational approximation for high-dimensional empirical Bayes posteriors.

The paper analyzes high-dimensional linear regression using parametric empirical Bayes methods.

New estimators outperform maximum likelihood without hyper-parameter estimation.

New method for high-dimensional linear regression using empirical Bayes.

Solution to sparse PCA tuning problem using Empirical Bayes.

New method predicts binary matrix entries using empirical Bayes and low-rank structure.

Flexible empirical Bayes for large-scale multiple linear regression.

New method uses SURE to denoise signals, outperforming NPMLE.

EB-PCA reduces noise in high-dimensional PCA by estimating a joint prior distribution.

Transformers solve Poisson means estimation via empirical Bayes.

Empirical Bayes improves causal representation learning across multiple domains.

Meta-learning framework improves model performance on few-shot classification tasks.

Efficient Bayesian LMM framework for high-dimensional longitudinal data.

A new sequential method estimates Poisson means in streaming data, achieving optimality and efficiency.

Efficiently selects important variables in high-dimensional logistic regression.

Unified empirical and variational Bayes for unnormalized densities.

We unify $\textit{kernel density estimation}$ and $\textit{empirical Bayes}$ and address a set of problems in unsupervised learning with a geometric interpretation of those methods, rooted in the $\textit{concentration of measure}$ phenomenon. Kernel density is viewed symbolically as $X\rightharpoonup Y$ where the rand…

Empirical Bayes method improves Gaussian sequence model inference.

We rebias estimates to improve interval calibration and prediction accuracy.

PROBE algorithm efficiently solves sparse high-dimensional linear regression.

Enhances normal mean estimation with side info using NIT approach.

We propose an empirical Bayes estimator based on Dirichlet process mixture model for estimating the sparse normalized mean difference, which could be directly applied to the high dimensional linear classification. In theory, we build a bridge to connect the estimation error of the mean difference and the misclassificat…

Stochastic variational inference for Bayesian deep neural network (DNN) requires specifying priors and approximate posterior distributions over neural network weights. Specifying meaningful weight priors is a challenging problem, particularly for scaling variational inference to deeper architectures involving high dime…

In this article we select the unknown dimension of the feature by re- versible jump MCMC inside a simulated annealing in bayesian set up of collaborative filter. We implement the same in MovieLens small dataset. We also tune the hyper parameter by using a modified empirical bayes. It can also be used to guess an initia…

We study the Nonparametric Maximum Likelihood Estimator (NPMLE) for estimating Gaussian location mixture densities in $d$-dimensions from independent observations. Unlike usual likelihood-based methods for fitting mixtures, NPMLEs are based on convex optimization. We prove finite sample results on the Hellinger accurac…

In this article we provide a formulation of empirical bayes described by Atchade (2011) to tune the hyperparameters of priors used in bayesian set up of collaborative filter. We implement the same in MovieLens small dataset. We see that it can be used to get a good initial choice for the parameters. It can also be used…

Gradient-based optimization improves variational empirical Bayes regression.

New insights into empirical Bayes and compound decision problems with improved regret bounds.

Paper analyzes consistency of Bayesian and machine learning methods for hierarchical parameter estimation.

Transformer pretraining yields strong EB performance without explicit adaptation.

The paper studies sparsity in EBF with hyperpriors and proposes a PALM algorithm.

We develop an empirical Bayes (EB) algorithm for the matrix completion problems. The EB algorithm is motivated from the singular value shrinkage estimator for matrix means by Efron and Morris (1972). Since the EB algorithm is essentially the EM algorithm applied to a simple model, it does not require heuristic paramete…

Estimates score function from data with optimal rate in high dimensions.

Proposes OBS, a method to adaptively combine Bayesian models online.

Most bandit algorithm designs are purely theoretical. Therefore, they have strong regret guarantees, but also are often too conservative in practice. In this work, we pioneer the idea of algorithm design by minimizing the empirical Bayes regret, the average regret over problem instances sampled from a known distributio…

Nonparametric empirical Bayes denoising on Riemannian manifolds

Proposes a new method to learn meta-priors from data.

Bayesian model for multi-environment prediction with latent variable changes.

Flexible Bayesian approach for generalized linear models, especially for sparse logistic regression.

LLEB uses a learned prior to quantify neural network uncertainty.

Attention-only transformers learn from context via two stages of inference.

Raising statistical hurdles may not be justified due to data bias.

We study methods for simultaneous analysis of many noisy experiments in the presence of rich covariate information. The goal of the analyst is to optimally estimate the true effect underlying each experiment. Both the noisy experimental results and the auxiliary covariates are useful for this purpose, but neither data …

Bayesian predictive inference analyzes a dataset to make predictions about new observations. When a model does not match the data, predictive accuracy suffers. We develop population empirical Bayes (POP-EB), a hierarchical framework that explicitly models the empirical population distribution as part of Bayesian analys…