Title: Determinantal Point Processes for Mini-Batch Diversification,
Abstract: We study a mini-batch diversification scheme for stochastic gradient descent
(SGD). While classical SGD relies on uniformly sampling data points to form a
mini-batch, we propose a non-uniform sampling scheme based on the Determinantal
Point Process (DPP). The DPP relies on a similarity measure between data points
and gives low probabilities to mini-batches which contain redundant data, and
higher probabilities to mini-batches with more diverse data. This
simultaneously balances the data and leads to stochastic gradients with lower
variance. We term this approach Diversified Mini-Batch SGD (DM-SGD). We show
that regular SGD and a biased version of stratified sampling emerge as special
cases. Furthermore, DM-SGD generalizes stratified sampling to cases where no
discrete features exist to bin the data into groups. We show experimentally
that our method results more interpretable and diverse features in unsupervised
setups, and in better classification accuracies in supervised setups.