  Finding valuable training data points for deep neural networks has been a
core research challenge with many applications. In recent years, various
techniques for calculating the "value" of individual training datapoints have
been proposed for explaining trained models. However, the value of a training
datapoint also depends on other selected training datapoints - a notion that is
not explicitly captured by existing methods. In this paper, we study the
problem of selecting high-value subsets of training data. The key idea is to
design a learnable framework for online subset selection, which can be learned
using mini-batches of training data, thus making our method scalable. This
results in a parameterized convex subset selection problem that is amenable to
a differentiable convex programming paradigm, thus allowing us to learn the
parameters of the selection model in end-to-end training. Using this framework,
we design an online alternating minimization-based algorithm for jointly
learning the parameters of the selection model and ML model. Extensive
evaluation on a synthetic dataset, and three standard datasets, show that our
algorithm finds consistently higher value subsets of training data, compared to
the recent state-of-the-art methods, sometimes ~20% higher value than existing
methods. The subsets are also useful in finding mislabelled training data. Our
algorithm takes running time comparable to the existing valuation functions.
