arXiv research

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

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

New Bayesian method for joint sparse parameter inference.

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

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.

A method for efficient CV estimates in Bayesian hierarchical models.

New MIF architecture improves posterior approximations in Bayesian models.

Common statistical practice has shown that the full power of Bayesian methods is not realized until hierarchical priors are used, as these allow for greater "robustness" and the ability to "share statistical strength." Yet it is an ongoing challenge to provide a learning-theoretically sound formalism of such notions th…

BOSH optimizes functions with stochastic evaluations more efficiently and precisely.

BoRA finetunes multi-task LLMs by sharing information through hierarchical priors.

A novel hierarchical Bayesian approach to Federated Learning reduces data exposure and improves convergence rates.

Study quantifies information borrowing in hierarchical Bayesian models.

New method groups genetic data into coherent topics for disease insights.

Bayesian models use hyperparameters to indirectly assign priors, and this work shows how these priors can be derived from maximum entropy principles.

Hierarchical IBP model for Bayesian neural networks in continual learning.

Piecewise constant denoising can be solved either by deterministic optimization approaches, based on the Potts model, or by stochastic Bayesian procedures. The former lead to low computational time but require the selection of a regularization parameter, whose value significantly impacts the achieved solution, and whos…

The problem of low rank matrix completion is considered in this paper. To exploit the underlying low-rank structure of the data matrix, we propose a hierarchical Gaussian prior model, where columns of the low-rank matrix are assumed to follow a Gaussian distribution with zero mean and a common precision matrix, and a W…

Hierarchical causal models help understand cause and effect in nested data.

Usually one compares the accuracy of two competing classifiers via null hypothesis significance tests (nhst). Yet the nhst tests suffer from important shortcomings, which can be overcome by switching to Bayesian hypothesis testing. We propose a Bayesian hierarchical model which jointly analyzes the cross-validation res…

The Dirichlet process and its extension, the Pitman-Yor process, are stochastic processes that take probability distributions as a parameter. These processes can be stacked up to form a hierarchical nonparametric Bayesian model. In this article, we present efficient methods for the use of these processes in this hierar…

Analyzing and understanding the structure of complex relational data is important in many applications including analysis of the connectivity in the human brain. Such networks can have prominent patterns on different scales, calling for a hierarchically structured model. We propose two non-parametric Bayesian hierarchi…

The paper develops a decision support system for hierarchical text classification of conference proceedings.

A new method for Bayesian neural networks using probabilistic backpropagation.

Bayesian models forecast COVID-19 hospitalizations at single sites.

Proposes a new Bayesian score for learning network structure from related datasets.

Deep learning method for comparing hierarchical models.

A new Bayesian model improves forecasting for intermittent demand.

Proposes a hierarchical model for learning discrete Bayesian networks with shrinkage.

Bayesian estimators for causal inference using hierarchical Gaussian Processes.

A new framework generates large hierarchical search spaces for neural architectures.

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

Hierarchical structure is ubiquitous in data across many domains. There are many hierarchical clustering methods, frequently used by domain experts, which strive to discover this structure. However, most of these methods limit discoverable hierarchies to those with binary branching structure. This limitation, while com…

This paper focuses on the problem of hierarchical non-overlapping clustering of a dataset. In such a clustering, each data item is associated with exactly one leaf node and each internal node is associated with all the data items stored in the sub-tree beneath it, so that each level of the hierarchy corresponds to a pa…

The cooperative hierarchical structure is a common and significant data structure observed in, or adopted by, many research areas, such as: text mining (author-paper-word) and multi-label classification (label-instance-feature). Renowned Bayesian approaches for cooperative hierarchical structure modeling are mostly bas…

We present a case-study demonstrating the usefulness of Bayesian hierarchical mixture modelling for investigating cognitive processes. In sentence comprehension, it is widely assumed that the distance between linguistic co-dependents affects the latency of dependency resolution: the longer the distance, the longer the …

Existing methods for sparse channel estimation typically provide an estimate computed as the solution maximizing an objective function defined as the sum of the log-likelihood function and a penalization term proportional to the l1-norm of the parameter of interest. However, other penalization terms have proven to have…

Bayesian model identifies skill difficulties and student subgroups in engineering education.

Bayesian model estimates feature values of premium products.

There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series data. However, in many settings the HDP-HMM's strict Markovian constraints are undesirable, particul…

The paper improves SBI for BHMs by diagnosing misspecification and inferring parameters.

Bayesian nonparametric models improve OOD detection, especially with complex covariance structures.

Items in modern recommender systems are often organized in hierarchical structures. These hierarchical structures and the data within them provide valuable information for building personalized recommendation systems. In this paper, we propose a general hierarchical Bayesian learning framework, i.e., \emph{HBayes}, to …

Hierarchical models are versatile tools for joint modeling of data sets arising from different, but related, sources. Fully Bayesian inference may, however, become computationally prohibitive if the source-specific data models are complex, or if the number of sources is very large. To facilitate computation, we propose…

The recently developed variational autoencoders (VAEs) have proved to be an effective confluence of the rich representational power of neural networks with Bayesian methods. However, most work on VAEs use a rather simple prior over the latent variables such as standard normal distribution, thereby restricting its appli…

The use of mutual information as a similarity measure in agglomerative hierarchical clustering (AHC) raises an important issue: some correction needs to be applied for the dimensionality of variables. In this work, we formulate the decision of merging dependent multivariate normal variables in an AHC procedure as a Bay…

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