  Many machine learning solutions are framed as optimization problems which
rely on good hyperparameters. Algorithms for tuning these hyperparameters
usually assume access to exact solutions to the underlying learning problem,
which is typically not practical. Here, we apply a recent dynamic accuracy
derivative-free optimization method to hyperparameter tuning, which allows
inexact evaluations of the learning problem while retaining convergence
guarantees. We test the method on the problem of learning elastic net weights
for a logistic classifier, and demonstrate its robustness and efficiency
compared to a fixed accuracy approach. This demonstrates a promising approach
for hyperparameter tuning, with both convergence guarantees and practical
performance.
