# The Vizier Gaussian Process Bandit Algorithm

arXiv: https://arxiv.org/abs/2408.11527 · HTML: https://mlsanity.com/paper/2408.11527/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Builds on (most cited first, 15 of 15)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2006.05078/) (2020)
- [Unexpected Improvements to Expected Improvement for Bayesian Optimization](https://mlsanity.com/paper/2310.20708/) (2023)
- [High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces](https://mlsanity.com/paper/2103.00349/) (2021)
- [TensorFlow Distributions](https://mlsanity.com/paper/1711.10604/) (2017)
- [HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation](https://mlsanity.com/paper/2012.03826/) (2020)
- [Combinatorial Bayesian Optimization using the Graph Cartesian Product](https://mlsanity.com/paper/1902.00448/) (2019)
- [Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization](https://mlsanity.com/paper/2006.04655/) (2020)
- [Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration](https://mlsanity.com/paper/1304.5350/) (2013)
- [Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization](https://mlsanity.com/paper/2210.10199/) (2022)
- [Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization](https://mlsanity.com/paper/2207.13676/) (2022)
- [COCO: The Large Scale Black-Box Optimization Benchmarking (bbob-largescale) Test Suite](https://mlsanity.com/paper/1903.06396/) (2019)
- [Optimal Scalarizations for Sublinear Hypervolume Regret](https://mlsanity.com/paper/2307.03288/) (2023)
- [How priors of initial hyperparameters affect Gaussian process regression models](https://mlsanity.com/paper/1605.07906/) (2016)

## Built on by (most cited first, 14 of 14)

- [Performance Prediction for Large Systems via Text-to-Text Regression](https://mlsanity.com/paper/2506.21718/) (2025)
- [Language Model Embeddings Can Be Sufficient for Bayesian Optimization](https://mlsanity.com/paper/2410.10190/) (2024)
- [Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation](https://mlsanity.com/paper/2410.16893/) (2024)
- [Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings](https://mlsanity.com/paper/2506.07109/) (2025)
- [Optimizing Pre-Training Data Mixtures with Mixtures of Data Expert Models](https://mlsanity.com/paper/2502.15950/) (2025)
- [RoboBallet: Planning for Multi-Robot Reaching with Graph Neural Networks and Reinforcement Learning](https://mlsanity.com/paper/2509.05397/) (2025)
- [Deterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?](https://mlsanity.com/paper/2503.03625/) (2025)
- [Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives](https://mlsanity.com/paper/2505.21598/) (2025)
- [FoMEMO: Towards Foundation Models for Expensive Multi-objective Optimization](https://mlsanity.com/paper/2509.03244/) (2025)
- [ZeroShotOpt: Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization](https://mlsanity.com/paper/2510.03051/) (2025)
- [SemanticOpt: Towards LLM-Based Semantic Black-Box Optimization](https://mlsanity.com/paper/2510.25404/) (2025)
- [Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence](https://mlsanity.com/paper/2608.12290/) (2026)
- [ALF: Advertiser Large Foundation Model for Multi-Modal Advertiser Understanding](https://mlsanity.com/paper/2504.18785/) (2025)
- [Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL](https://mlsanity.com/paper/2607.26680/) (2026)

## Abstract (arXiv)

Google Vizier has performed millions of optimizations and accelerated numerous research and production systems at Google, demonstrating the success of Bayesian optimization as a large-scale service. Over multiple years, its algorithm has been improved considerably, through the collective experiences of numerous research efforts and user feedback. In this technical report, we discuss the implementation details and design choices of the current default algorithm provided by Open Source Vizier. Our experiments on standardized benchmarks reveal its robustness and versatility against well-established industry baselines on multiple practical modes.