We study the effects of increasing the batch size on training time, as measured by the number of steps necessary to reach a goal out-of-sample error.Expand

This is an extended and updated version of our conference paper that appeared in Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 2015.Expand

In many estimation problems, e.g. linear and logistic regression, we wish to minimize an unknown objective given only unbiased samples of the objective function.Expand

We describe JAX, a domain-specific tracing JIT compiler for generating high-performance accelerator code from pure Python and Numpy machine learning programs, capable of scaling to multi-core Cloud TPUs.Expand

We focus on the problem of maximum a posteriori (MAP) inference in Markov random fields with binary variables and pairwise interactions with low-rank relaxations that interpolate between the discrete problem and its full-rank semidefinite relaxation.Expand

Identity-by-descent (IBD) inference is the problem of establishing a genetic connection between two individuals through a genomic segment that is inherited by both individuals from a recent commonâ€¦ Expand

We show a new upper bound of $\tilde O(\max\{\sqrt{k\log(n)/(mn)},k/n\})$ on the worst-case bias that any attack can achieve in a prediction problem with $m$ classes.Expand