# Fully Distributed Bayesian Optimization with Stochastic Policies

arXiv: https://arxiv.org/abs/1902.09992 · HTML: https://mlsanity.com/paper/1902.09992/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2019.

## Built on by (most cited first, 9 of 9)

- [Recent Advances in Bayesian Optimization](https://mlsanity.com/paper/2206.03301/) (2022)
- [Differentially Private Federated Bayesian Optimization with Distributed Exploration](https://mlsanity.com/paper/2110.14153/) (2021)
- [Gaussian Max-Value Entropy Search for Multi-Agent Bayesian Optimization](https://mlsanity.com/paper/2303.05694/) (2023)
- [Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization](https://mlsanity.com/paper/2207.00479/) (2022)
- [TS-RSR: A provably efficient approach for batch Bayesian Optimization](https://mlsanity.com/paper/2403.04764/) (2024)
- [Agent-based Collaborative Random Search for Hyper-parameter Tuning and Global Function Optimization](https://mlsanity.com/paper/2303.03394/) (2023)
- [Distributed Thompson sampling under constrained communication](https://mlsanity.com/paper/2410.15543/) (2024)
- [Randomized Kriging Believer for Parallel Bayesian Optimization with Regret Bounds](https://mlsanity.com/paper/2603.01470/) (2026)
- [B3O: Scalable Boltzmann Batch Bayesian Optimization](https://mlsanity.com/paper/2606.30228/) (2026)

## Abstract (arXiv)

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. Thompson sampling has been previously applied for distributed Bayesian optimization. But, when compared with other acquisition functions in the sequential setting, Thompson sampling is known to perform suboptimally. In this paper, we present a new method for fully distributed Bayesian optimization, which can be combined with any acquisition function. Our approach considers Bayesian optimization as a partially observable Markov decision process. In this context, stochastic policies, such as the Boltzmann policy, have some interesting properties which can also be studied for Bayesian optimization. Furthermore, the Boltzmann policy trivially allows a distributed Bayesian optimization implementation with high level of parallelism and scalability. We present results in several benchmarks and applications that shows the performance of our method.