# Contextual Bayesian optimization with binary outputs

arXiv: https://arxiv.org/abs/2111.03447 · HTML: https://mlsanity.com/paper/2111.03447/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 48 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 16 of 16)

- [Predictive Entropy Search for Efficient Global Optimization of Black-box Functions](https://mlsanity.com/paper/1406.2541/) (2014)
- [Batched Large-scale Bayesian Optimization in High-dimensional Spaces](https://mlsanity.com/paper/1706.01445/) (2017)
- [Contextual Dueling Bandits](https://mlsanity.com/paper/1502.06362/) (2015)
- [Hilbert Space Methods for Reduced-Rank Gaussian Process Regression](https://mlsanity.com/paper/1401.5508/) (2014)
- [The Parallel Knowledge Gradient Method for Batch Bayesian Optimization](https://mlsanity.com/paper/1606.04414/) (2016)
- [Preferential Bayesian Optimization](https://mlsanity.com/paper/1704.03651/) (2017)
- [Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming](https://mlsanity.com/paper/2004.11408/) (2020)
- [Bayesian Optimization with Expensive Integrands](https://mlsanity.com/paper/1803.08661/) (2018)
- [Bayesian Optimization with Binary Auxiliary Information](https://mlsanity.com/paper/1906.07277/) (2019)
- [Deep Kernels for Optimizing Locomotion Controllers](https://mlsanity.com/paper/1707.09062/) (2017)
- [Correlational Dueling Bandits with Application to Clinical Treatment in Large Decision Spaces](https://mlsanity.com/paper/1707.02375/) (2017)
- [Sequential Preference-Based Optimization](https://mlsanity.com/paper/1801.02788/) (2018)
- [Efficient Exploration in Binary and Preferential Bayesian Optimization](https://mlsanity.com/paper/2110.09361/) (2021)
- [A Stratified Analysis of Bayesian Optimization Methods](https://mlsanity.com/paper/1603.09441/) (2016)
- [Factored Contextual Policy Search with Bayesian Optimization](https://mlsanity.com/paper/1612.01746/) (2016)
- [Practical Bayesian Optimization of Objectives with Conditioning Variables](https://mlsanity.com/paper/2002.09996/) (2020)

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

- [Contextual Causal Bayesian Optimisation](https://mlsanity.com/paper/2301.12412/) (2023)

## Abstract (arXiv)

Bayesian optimization (BO) is an efficient method to optimize expensive black-box functions. It has been generalized to scenarios where objective function evaluations return stochastic binary feedback, such as success/failure in a given test, or preference between different parameter settings. In many real-world situations, the objective function can be evaluated in controlled 'contexts' or 'environments' that directly influence the observations. For example, one could directly alter the 'difficulty' of the test that is used to evaluate a system's performance. With binary feedback, the context determines the information obtained from each observation. For example, if the test is too easy/hard, the system will always succeed/fail, yielding uninformative binary outputs. Here we combine ideas from Bayesian active learning and optimization to efficiently choose the best context and optimization parameter on each iteration. We demonstrate the performance of our algorithm and illustrate how it can be used to tackle a concrete application in visual psychophysics: efficiently improving patients' vision via corrective lenses, using psychophysics measurements.