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Found 9 papers, 2 papers with code
Bayesian Inference with the l1-ball Prior: Solving Combinatorial Problems with Exact Zeros
Using a continuous prior concentrated near zero, the Bayesian counterparts are successful in quantifying the uncertainty in the variable selection problems; nevertheless, the lack of exact zeros makes it difficult for broader problems such as the change-point detection and rank selection.
Methodology
Transport Monte Carlo: High-Accuracy Posterior Approximation via Random Transport
In Bayesian applications, there is a huge interest in rapid and accurate estimation of the posterior distribution, particularly for high dimensional or hierarchical models.
Computation Methodology
Tuning-Free Disentanglement via Projection
In representation learning and non-linear dimension reduction, there is a huge interest to learn the 'disentangled' latent variables, where each sub-coordinate almost uniquely controls a facet of the observed data.
Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification
High dimensional data often contain multiple facets, and several clustering patterns can co-exist under different variable subspaces, also known as the views.
Bayesian Distance Clustering
Model-based clustering is widely-used in a variety of application areas.
Mixed-Stationary Gaussian Process for Flexible Non-Stationary Modeling of Spatial Outcomes
Different from a simple mixture of independent GPs, the mixture in stationarity allows the components to be spatial correlated, leading to improved prediction efficiency.
Functional Gaussian Process Model for Bayesian Nonparametric Analysis
Gaussian process is a theoretically appealing model for nonparametric analysis, but its computational cumbersomeness hinders its use in large scale and the existing reduced-rank solutions are usually heuristic.
Joint Hierarchical Gaussian Process Model with Application to Forecast in Medical Monitoring
Keyword: Extrapolation, Joint Model, Longitudinal Model, Hierarchical Gaussian Process, Cystic Fibrosis, Medical Monitoring
BET: Bayesian Ensemble Trees for Clustering and Prediction in Heterogeneous Data
We propose a novel "tree-averaging" model that utilizes the ensemble of classification and regression trees (CART).
