arXiv research

A method for efficient CV estimates in Bayesian hierarchical models.

Bayesian estimators for causal inference using hierarchical Gaussian Processes.

A new method for Bayesian neural networks using probabilistic backpropagation.

Develops a Bayesian framework for symbolic regression of scientific expressions.

We propose a nonparametric Bayesian factor regression model that accounts for uncertainty in the number of factors, and the relationship between factors. To accomplish this, we propose a sparse variant of the Indian Buffet Process and couple this with a hierarchical model over factors, based on Kingman's coalescent. We…

A scalable Bayesian linear regression framework for spatial data.

HBR improves normative modeling of neuroimaging data across multiple sites.

Paper proposes a new method for Bayesian linear regression using spike-and-slab priors.

In this paper a new Bayesian model for sparse linear regression with a spatio-temporal structure is proposed. It incorporates the structural assumptions based on a hierarchical Gaussian process prior for spike and slab coefficients. We design an inference algorithm based on Expectation Propagation and evaluate the mode…

metabeta uses neural networks to speed up Bayesian mixed-effects regression.

Novel Bayesian framework for spatio-temporal neuroimaging data.

A new method for efficient BNC parameter estimation outperforms HDP smoothing.

SmallML predicts customer churn for SMEs with small data, improving accuracy by 24.2 points.

Bayesian approach for learning spatiotemporal systems from noisy data.

New hierarchical model improves on standard practice for high-dimensional data.

The Bayesian Lasso is constructed in the linear regression framework and applies the Gibbs sampling to estimate the regression parameters. This paper develops a new sparse learning model, named the Bayesian Lasso Sparse (BLS) model, that takes the hierarchical model formulation of the Bayesian Lasso. The main differenc…

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…

The SLOPE estimates regression coefficients by minimizing a regularized residual sum of squares using a sorted-$\ell_1$-norm penalty. The SLOPE combines testing and estimation in regression problems. It exhibits suitable variable selection and prediction properties, as well as minimax optimality. This paper introduces …

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

In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike existing deep learning libraries, which are mainly designed for deterministic neural netw…

We investigate the choice of tuning parameters for a Bayesian multi-level group lasso model developed for the joint analysis of neuroimaging and genetic data. The regression model we consider relates multivariate phenotypes consisting of brain summary measures (volumetric and cortical thickness values) to single nucleo…

Study on Metropolis-within-Gibbs schemes for high-dimensional Bayesian models.

Paper develops a dynamic Bayesian approach for active learning that optimizes exploration-exploitation balance.

We propose the supervised hierarchical Dirichlet process (sHDP), a nonparametric generative model for the joint distribution of a group of observations and a response variable directly associated with that whole group. We compare the sHDP with another leading method for regression on grouped data, the supervised latent…

Bayesian Hierarchical Invariant Prediction refines ICP for better scalability and prior integration.

This paper introduces a new sparse spatio-temporal structured Gaussian process regression framework for online and offline Bayesian inference. This is the first framework that gives a time-evolving representation of the interdependencies between the components of the sparse signal of interest. A hierarchical Gaussian p…

Bayesian pliable lasso with horseshoe prior models interactions in GLMs with missing data.

One of the challenges in model-based control of stochastic dynamical systems is that the state transition dynamics are involved, and it is not easy or efficient to make good-quality predictions of the states. Moreover, there are not many representational models for the majority of autonomous systems, as it is not easy …

Improves sampling efficiency for complex Bayesian models.

Paper proposes fully Bayesian approach for RVM classification, improving accuracy especially in imbalanced data.

Posterior regularization enhances Bayesian hierarchical mixture clustering by improving node separation.

Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers. DGPs are nonparametric probabilistic models and as such are arguably more flexible, have a greater capacity to generalise, …

Proposes HDBEN for heteroscedastic regression with improved sparsity and variance modeling.

Learning in Gaussian Process models occurs through the adaptation of hyperparameters of the mean and the covariance function. The classical approach entails maximizing the marginal likelihood yielding fixed point estimates (an approach called \textit{Type II maximum likelihood} or ML-II). An alternative learning proced…

Motivation: Recent advances in technology for brain imaging and high-throughput genotyping have motivated studies examining the influence of genetic variation on brain structure. Wang et al. (Bioinformatics, 2012) have developed an approach for the analysis of imaging genomic studies using penalized multi-task regressi…

New Bayesian method for joint sparse parameter inference.

This work analyzes Gibbs samplers for Bayesian hierarchical models without dimensionality constraints.

Mean-field variational methods are widely used for approximate posterior inference in many probabilistic models. In a typical application, mean-field methods approximately compute the posterior with a coordinate-ascent optimization algorithm. When the model is conditionally conjugate, the coordinate updates are easily …

Paper proposes efficient methods for forecasting with large datasets.

The Hierarchical Mixture of Experts (HME) is a well-known tree-based model for regression and classification, based on soft probabilistic splits. In its original formulation it was trained by maximum likelihood, and is therefore prone to over-fitting. Furthermore the maximum likelihood framework offers no natural metri…

The paper revisits and improves on a Bayesian relevance vector machine method for small sample sizes.

Generalized linear models (GLMs) -- such as logistic regression, Poisson regression, and robust regression -- provide interpretable models for diverse data types. Probabilistic approaches, particularly Bayesian ones, allow coherent estimates of uncertainty, incorporation of prior information, and sharing of power acros…

GRASP simplifies Bayesian regression with grouped predictors using an adaptive NBP prior.

Develops Bayesian inference methods for gamma models.

Bayesian algorithm improves word representations using semantic taxonomy.

Bayesian stacking improves model performance with varying model weights.

Unified Bayesian framework for PTA data analysis tackles hierarchical model issues.

In this paper we discuss Bayesian nonconvex penalization for sparse learning problems. We explore a nonparametric formulation for latent shrinkage parameters using subordinators which are one-dimensional Lévy processes. We particularly study a family of continuous compound Poisson subordinators and a family of discrete…