Multilabel Classification for Entry-Dependent Expert Selection in Distributed Gaussian Processes
By distributing the training process, local approximation reduces the cost of the standard Gaussian process.An ensemble method aggregates predictions from local Gaussian experts, each trained on different data partitions, under the assumption of perfect diversity among them.While this assumption ensures tractable aggregation, it is frequently viola