Global optimization of expensive, potentially gradient-free functions has long been a critical component of many complex problems in science and engineering.

Utilizing mutual learning can effectively enhance the performance of peer BNNs.

Machine learning (ML) models have been widely used to make predictions.

A Bayesian pseudocoreset is a compact synthetic dataset summarizing essential information of a large-scale dataset and thus can be used as a proxy dataset for scalable Bayesian inference. Typically, a Bayesian pseudocoreset is constructed by minimizing a divergence measure between the posterior conditioning on the pseudocoreset and the posterior conditioning on the full dataset. However, evaluating the divergence can be challenging, particularly for the models like deep neural networks having high-dimensional parameters.