For example, gradient descent is often used in machine learning in ways that don’t require extreme precision. But a machine learning researcher might want to double the precision of an experiment. In ...

The strategic advantage of QML continues to expand its presence in industries that deal with complex, high-dimensional data.

Ben Grimmer showed that gradient descent algorithms can work faster by including unexpectedly large step sizes — the opposite of what researchers long believed. Will Kirk “It turns out that we did not ...

However, the gradient descent algorithms need to update variables one by one to calculate the loss function with each iteration, which leads to a large amount of computation and a long training time.

We propose and study a new iterative coordinate descent algorithm (QICD) for solving nonconvex penalized quantile regression in high dimension. By permitting different subsets of covariates to be ...