Contextual Bayesian optimization with binary outputs

TL;DR

This paper introduces a Bayesian optimization method that accounts for contextual information and binary feedback, enabling efficient optimization in scenarios where observations depend on controllable environments.

Contribution

It presents a novel approach combining Bayesian active learning with contextual information to optimize binary feedback functions more effectively.

Findings

The method outperforms existing approaches in simulated tests.

Application to visual psychophysics demonstrates practical utility.

Efficiently improves patients' vision using binary feedback and context control.

Abstract

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.