Gradient Free

Gradient-free optimization methods address the challenges of training complex models, particularly deep neural networks, where gradient computation is infeasible or computationally expensive. Current research focuses on applying these methods to diverse problems, including active learning, robotic control, and hyperparameter tuning, often employing algorithms like evolutionary strategies, zeroth-order methods, and coordinate search. This field is significant because it expands the applicability of machine learning to scenarios with non-differentiable objectives or limited access to model internals, impacting areas such as black-box optimization and resource-constrained environments. The development of efficient gradient-free techniques promises to improve the scalability and robustness of machine learning across various domains.