# Evaluation System for a Bayesian Optimization Service

arXiv: https://arxiv.org/abs/1605.06170 · HTML: https://mlsanity.com/paper/1605.06170/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2016.

## Built on by (most cited first, 1 of 1)

- [A Light-Weight Multi-Objective Asynchronous Hyper-Parameter Optimizer](https://mlsanity.com/paper/2202.07735/) (2022)

## Abstract (arXiv)

Bayesian optimization is an elegant solution to the hyperparameter optimization problem in machine learning. Building a reliable and robust Bayesian optimization service requires careful testing methodology and sound statistical analysis. In this talk we will outline our development of an evaluation framework to rigorously test and measure the impact of changes to the SigOpt optimization service. We present an overview of our evaluation system and discuss how this framework empowers our research engineers to confidently and quickly make changes to our core optimization engine