  Large-scale supervised classification algorithms, especially those based on
deep convolutional neural networks (DCNNs), require vast amounts of training
data to achieve state-of-the-art performance. Decreasing this data requirement
would significantly speed up the training process and possibly improve
generalization. Motivated by this objective, we consider the task of adaptively
finding concise training subsets which will be iteratively presented to the
learner. We use convex optimization methods, based on an objective criterion
and feedback from the current performance of the classifier, to efficiently
identify informative samples to train on. We propose an algorithm to decompose
the optimization problem into smaller per-class problems, which can be solved
in parallel. We test our approach on standard classification tasks and
demonstrate its effectiveness in decreasing the training set size without
compromising performance. We also show that our approach can make the
classifier more robust in the presence of label noise and class imbalance.
