Bayesian Optimization with Preference Exploration using a Monotonic Neural Network Ensemble

TL;DR

This paper introduces a Bayesian optimization method that leverages monotonic neural network ensembles for preference exploration, improving efficiency and robustness in multi-objective optimization tasks with noisy utility data.

Contribution

It proposes a novel utility surrogate model using monotonic neural network ensembles that effectively incorporate preference information and monotonicity constraints.

Findings

Outperforms state-of-the-art methods in experiments.

Shows robustness to noise in utility evaluations.

Highlights importance of monotonicity for performance.

Abstract

Many real-world black-box optimization problems have multiple conflicting objectives. Rather than attempting to approximate the entire set of Pareto-optimal solutions, interactive preference learning allows to focus the search on the most relevant subset. However, few previous studies have exploited the fact that utility functions are usually monotonic. In this paper, we address the Bayesian Optimization with Preference Exploration (BOPE) problem and propose using a neural network ensemble as a utility surrogate model. This approach naturally integrates monotonicity and supports pairwise comparison data. Our experiments demonstrate that the proposed method outperforms state-of-the-art approaches and exhibits robustness to noise in utility evaluations. An ablation study highlights the critical role of monotonicity in enhancing performance.