Asynchronous parallel Bayesian optimization for AI-driven cloud laboratories.

ABSTRACT:

Motivation

The recent emergence of cloud laboratories-collections of automated wet-lab instruments that are accessed remotely, presents new opportunities to apply Artificial Intelligence and Machine Learning in scientific research. Among these is the challenge of automating the process of optimizing experimental protocols to maximize data quality.

Results

We introduce a new deterministic algorithm, called PaRallel OptimizaTiOn for ClOud Laboratories (PROTOCOL), that improves experimental protocols via asynchronous, parallel Bayesian optimization. The algorithm achieves exponential convergence with respect to simple regret. We demonstrate PROTOCOL in both simulated and real-world cloud labs. In the simulated lab, it outperforms alternative approaches to Bayesian optimization

SUBMITTER: Frisby TS 

PROVIDER: S-EPMC8275326 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature