Efficient Hyperparameter Tuning with Dynamic Accuracy Derivative-Free Optimization
This work addresses hyperparameter tuning for machine learning practitioners by offering a more practical method with convergence guarantees, though it is incremental as it adapts an existing optimization technique to this domain.
The paper tackled the problem of hyperparameter tuning in machine learning by applying a dynamic accuracy derivative-free optimization method that allows inexact evaluations while maintaining convergence guarantees, demonstrating robustness and efficiency on elastic net weights for logistic classifiers compared to fixed accuracy approaches.
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.