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Method and system for parallel batch processing of data sets using Gaussian process with batch upper confidence bound

机译:使用具有批次上置信界的高斯过程并行处理数据集的方法和系统

摘要

A method and system for selecting a batch of input data from available input data for parallel evaluation by a function is disclosed. The function is modeled as drawn from a Gaussian process. Observations are used to determine a mean and a variance of the modeled function. An upper confidence bound is determined from the determined mean and variance. A decision rule is applied to select input data from the available input data to add to the batch of input data. The selection of the input data is based on a domain-specific time varying parameter. Intermediate observations are hallucinated within the batch. The hallucinated observations are used with the decision rule to select subsequent input data from the available input data for the batch of input data. The input data of the batch is evaluated in parallel with the function. The resulting determined data outputs are stored.
机译:公开了一种用于从可用输入数据中选择一批输入数据以通过功能进行并行评估的方法和系统。该函数是根据高斯过程绘制的。观察值用于确定建模函数的均值和方差。从确定的均值和方差确定置信上限。应用决策规则从可用输入数据中选择输入数据,以添加到该批输入数据中。输入数据的选择基于特定于域的时变参数。中间观察结果在批处理中产生幻觉。幻觉观测与决策规则一起使用,以从可用输入数据中为该批输入数据选择后续输入数据。批处理的输入数据与功能并行进行评估。存储确定的结果数据输出。

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