Adaptive sampling of training data for machine learning models based on PAC-bayes analysis of risk bounds

摘要

At a machine learning service, an indication of a training data set for a model is obtained. One or more training iterations of the model are conducted using an adaptive input sampling strategy. In a particular iteration, index values for a set of training observations are selected based on a set of sampling weights, parameters of the model are updated based on results using training observations identified by the index values, and sampling weights are modified. A result obtained from a trained version of the machine learning model is provided.