# High-dimensional Bayesian optimization using low-dimensional feature spaces

## TL;DR

This paper introduces a novel Bayesian optimization method that learns a low-dimensional feature space jointly with the response surface and reconstruction mapping, enabling efficient high-dimensional optimization within a small evaluation budget.

## Contribution

It proposes a joint learning approach for low-dimensional embeddings and response surfaces, improving high-dimensional BO scalability with limited data.

## Key findings

- Enables optimization in high-dimensional spaces using learned low-dimensional features.
- Improves efficiency of BO by reducing the optimization complexity.
- Supports meaningful exploration through constrained optimization.

## Abstract

Bayesian optimization (BO) is a powerful approach for seeking the global optimum of expensive black-box functions and has proven successful for fine tuning hyper-parameters of machine learning models. However, BO is practically limited to optimizing 10--20 parameters. To scale BO to high dimensions, we usually make structural assumptions on the decomposition of the objective and\slash or exploit the intrinsic lower dimensionality of the problem, e.g. by using linear projections. We could achieve a higher compression rate with nonlinear projections, but learning these nonlinear embeddings typically requires much data. This contradicts the BO objective of a relatively small evaluation budget. To address this challenge, we propose to learn a low-dimensional feature space jointly with (a) the response surface and (b) a reconstruction mapping. Our approach allows for optimization of BO's acquisition function in the lower-dimensional subspace, which significantly simplifies the optimization problem. We reconstruct the original parameter space from the lower-dimensional subspace for evaluating the black-box function. For meaningful exploration, we solve a constrained optimization problem.