Neural Global Optimization via Iterative Refinement from Noisy Samples

ArXi:2604.03614v1 Announce Type: cross Global optimization of black-box functions from noisy samples is a fundamental challenge in machine learning and scientific computing. Traditional methods such as Bayesian Optimization often converge to local minima on multi-modal functions, while gradient-free methods require many function evaluations. We present a novel neural approach that learns to find global minima through iterative refinement.