  While stochastic gradient descent (SGD) is one of the major workhorses in
machine learning, the learning properties of many practically used variants are
poorly understood. In this paper, we consider least squares learning in a
nonparametric setting and contribute to filling this gap by focusing on the
effect and interplay of multiple passes, mini-batching and averaging, and in
particular tail averaging. Our results show how these different variants of SGD
can be combined to achieve optimal learning errors, hence providing practical
insights. In particular, we show for the first time in the literature that tail
averaging allows faster convergence rates than uniform averaging in the
nonparametric setting. Finally, we show that a combination of tail-averaging
and minibatching allows more aggressive step-size choices than using any one of
said components.
