Surrogate Objective

Surrogate objectives are stand-in functions used in optimization problems where the true objective is computationally expensive or difficult to measure directly. Current research focuses on improving the accuracy and efficiency of these surrogates, particularly addressing biases in gradient-based methods and developing robust algorithms for handling uncertainty in surrogate predictions, such as through adaptive regularization or early stopping techniques. This work has significant implications across diverse fields, including reinforcement learning, causal inference, and generative design, by enabling more efficient and reliable optimization in scenarios with limited or noisy data.