  Large scale machine learning and deep models are extremely data-hungry.
Unfortunately, obtaining large amounts of labeled data is expensive, and
training state-of-the-art models (with hyperparameter tuning) requires
significant computing resources and time. Secondly, real-world data is noisy
and imbalanced. As a result, several recent papers try to make the training
process more efficient and robust. However, most existing work either focuses
on robustness or efficiency, but not both. In this work, we introduce Glister,
a GeneraLIzation based data Subset selecTion for Efficient and Robust learning
framework. We formulate Glister as a mixed discrete-continuous bi-level
optimization problem to select a subset of the training data, which maximizes
the log-likelihood on a held-out validation set. Next, we propose an iterative
online algorithm Glister-Online, which performs data selection iteratively
along with the parameter updates and can be applied to any loss-based learning
algorithm. We then show that for a rich class of loss functions including
cross-entropy, hinge-loss, squared-loss, and logistic-loss, the inner discrete
data selection is an instance of (weakly) submodular optimization, and we
analyze conditions for which Glister-Online reduces the validation loss and
converges. Finally, we propose Glister-Active, an extension to batch active
learning, and we empirically demonstrate the performance of Glister on a wide
range of tasks including, (a) data selection to reduce training time, (b)
robust learning under label noise and imbalance settings, and (c) batch-active
learning with several deep and shallow models. We show that our framework
improves upon state of the art both in efficiency and accuracy (in cases (a)
and (c)) and is more efficient compared to other state-of-the-art robust
learning algorithms in case (b).
