DT-PBO: an Interpretable Tree-based Surrogate Model for Preferential Bayesian Optimization

📰 ArXiv cs.AI

Learn how DT-PBO, a tree-based surrogate model, improves interpretability in Preferential Bayesian Optimization, enabling better decision-making in high-stakes domains like healthcare

advanced Published 11 May 2026

Action Steps

Implement DT-PBO using a tree-based surrogate model to improve interpretability in PBO
Compare the performance of DT-PBO with existing GP-based approaches
Apply DT-PBO to a real-world problem in a high-stakes domain, such as healthcare
Evaluate the interpretability and trustworthiness of DT-PBO in the chosen domain
Refine the DT-PBO model based on the evaluation results

Who Needs to Know This

Data scientists and machine learning engineers working on Bayesian optimization and decision-making systems can benefit from this research, as it provides a more interpretable and trustworthy approach

Key Insight

💡 DT-PBO provides a more interpretable and trustworthy approach to Preferential Bayesian Optimization, enabling better decision-making in high-stakes domains

Key Takeaways

Learn how DT-PBO, a tree-based surrogate model, improves interpretability in Preferential Bayesian Optimization, enabling better decision-making in high-stakes domains like healthcare

Full Article

Title: DT-PBO: an Interpretable Tree-based Surrogate Model for Preferential Bayesian Optimization

Abstract:
arXiv:2512.14263v2 Announce Type: replace-cross Abstract: Preferential Bayesian Optimization (PBO) aims to find a decision-maker's most preferred solution in as few pairwise comparisons as possible. Existing approaches rely on Gaussian Process (GP) surrogates, which provide strong performance but limited interpretability. This limits real-world usability in high-stakes domains, such as healthcare, where interpretability and trust are essential. We propose DT-PBO, a novel tree-based surrogate mod

Read full paper → ← Back to Reads