  Recent years have witnessed amazing outcomes from "Big Models" trained by
"Big Data". Most popular algorithms for model training are iterative. Due to
the surging volumes of data, we can usually afford to process only a fraction
of the training data in each iteration. Typically, the data are either
uniformly sampled or sequentially accessed.
  In this paper, we study how the data access pattern can affect model
training. We propose an Active Sampler algorithm, where training data with more
"learning value" to the model are sampled more frequently. The goal is to focus
training effort on valuable instances near the classification boundaries,
rather than evident cases, noisy data or outliers. We show the correctness and
optimality of Active Sampler in theory, and then develop a light-weight
vectorized implementation. Active Sampler is orthogonal to most approaches
optimizing the efficiency of large-scale data analytics, and can be applied to
most analytics models trained by stochastic gradient descent (SGD) algorithm.
Extensive experimental evaluations demonstrate that Active Sampler can speed up
the training procedure of SVM, feature selection and deep learning, for
comparable training quality by 1.6-2.2x.
