New optimization method slashes sample requirements for machine learning applications.
Stochastic bilevel optimization is a type of problem where you need to find the best solution for one problem while considering another problem. A new method called STABLE has been developed to solve these types of problems more efficiently. STABLE uses a single loop and a fixed batch size to find solutions faster. It requires fewer samples to reach a good solution compared to existing methods. This new method achieves similar efficiency to standard optimization methods for simpler problems.