Diversity By Design: Leveraging Distribution Matching for Offline Model-Based Optimization
Michael S Yao, James Gee, Osbert Bastani
Abstract
The goal of offline model-based optimization (MBO) is to propose new designs that maximize a reward function given only an offline dataset. However, an important desiderata is to also propose a *diverse* set of final candidates that capture many optimal and near-optimal design configurations. We propose **D**iversit**y** I**n** **A**dversarial **M**odel-based **O**ptimization (**DynAMO**) as a novel method to introduce design diversity as an explicit objective into any MBO problem. Our key insight is to formulate diversity as a *distribution matching problem* where the distribution of generated designs captures the inherent diversity contained within the offline dataset. Extensive experiments spanning multiple scientific domains show that DynAMO can be used with common optimization methods to significantly improve the diversity of proposed designs while still discovering high-quality candidates.
BibTeX
@inproceedings{
yao2025diversity,
title={Diversity By Design: Leveraging Distribution Matching for Offline Model-Based Optimization},
author={Michael S Yao and James Gee and Osbert Bastani},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=nyXXE3EA1b}
}