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Found 5 papers, 1 papers with code
Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification
Ensemble methods aggregate models' predictions by assuming a perfect diversity of local predictors.
Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes
Distributed Gaussian process (DGP) is a popular approach to scale GP to big data which divides the training data into some subsets, performs local inference for each partition, and aggregates the results to acquire global prediction.
A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines
Understanding the results of deep neural networks is an essential step towards wider acceptance of deep learning algorithms.
Gaussian Experts Selection using Graphical Models
Imposing the \emph{conditional independence assumption} (CI) between the experts renders the aggregation of different expert predictions time efficient at the cost of poor uncertainty quantification.
Aggregating Dependent Gaussian Experts in Local Approximation
The precision matrix encodes conditional dependencies between experts and is used to detect strongly dependent experts and construct an improved aggregation.
