# Bayesian Optimization Using Monotonicity Information and Its Application in Machine Learning Hyperparameter

arXiv: https://arxiv.org/abs/1802.03532 · HTML: https://mlsanity.com/paper/1802.03532/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2018.

## Built on by (most cited first, 2 of 2)

- [Benefits of Monotonicity in Safe Exploration with Gaussian Processes](https://mlsanity.com/paper/2211.01561/) (2022)
- [Predictive Modeling through Hyper-Bayesian Optimization](https://mlsanity.com/paper/2308.00285/) (2023)

## Abstract (arXiv)

We propose an algorithm for a family of optimization problems where the objective can be decomposed as a sum of functions with monotonicity properties. The motivating problem is optimization of hyperparameters of machine learning algorithms, where we argue that the objective, validation error, can be decomposed as monotonic functions of the hyperparameters. Our proposed algorithm adapts Bayesian optimization methods to incorporate the monotonicity constraints. We illustrate the advantages of exploiting monotonicity using illustrative examples and demonstrate the improvements in optimization efficiency for some machine learning hyperparameter tuning applications.