Fully Distributed Bayesian Optimization with Stochastic Policies

Understand

Bayesian optimization has become a popular method for high-throughput computing, like the design of computer experiments or hyperparameter tuning of expensive models, where sample efficiency is mandatory.

• In these applications, distributed and scalable architectures are a necessity.
• However, Bayesian optimization is mostly sequential.
• Even parallel variants require certain computations between samples, limiting the parallelization bandwidth.