Diversity-Driven Offline Multi-Objective Optimization via Nested Pareto Set Learning

2026-06-13 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences · Depth: Expert, quick

Summary

Diversity-driven Offline Multi-Objective Optimization (DOMOO) is a novel approach designed to solve complex multi-objective optimization (MOO) problems using only fixed offline datasets, a setting where function evaluations are unavailable or expensive. This method specifically tackles the out-of-distribution (OOD) issue, which often leads surrogate models to select solutions biased towards Pareto front extremes and not on the true front. DOMOO incorporates an accumulative risk control module to estimate and mitigate potential risks from candidate solutions. It also introduces a nested Pareto set learning (PSL) strategy that jointly learns and optimizes preference and PSL parameters, adapting to various Pareto front geometries. Furthermore, DOMOO employs a diversity-driven selection strategy, utilizing $\text{IGD}_\text{offline}$ as a tailored indicator to extract representative and well-distributed final solutions, avoiding hypervolume indicator bias. Extensive experiments on synthetic and real-world benchmarks demonstrate DOMOO's superior average rank in both convergence and diversity compared to other methods.

Key takeaway

For Machine Learning Engineers developing offline multi-objective optimization solutions, you should integrate robust risk control and adaptive learning strategies. Implementing DOMOO's accumulative risk control module can alleviate out-of-distribution issues, ensuring more reliable surrogate model predictions. Furthermore, adopting a nested Pareto set learning strategy will enable your models to adapt effectively to diverse Pareto front geometries. Consider using $\text{IGD}_\text{offline}$ for selecting final solutions, as it balances diversity and convergence, leading to higher-quality, representative outcomes in your applications.

Key insights

Offline multi-objective optimization can achieve diverse, high-quality Pareto sets by mitigating OOD issues and learning nested preferences.

Principles

• Mitigate OOD issues in offline MOO.
• Risk control improves surrogate model accuracy.
• Nested learning adapts to Pareto front geometries.

Method

DOMOO incorporates an accumulative risk control module, a nested Pareto set learning (PSL) strategy for joint preference and PSL parameter optimization, and a diversity-driven selection strategy using $\text{IGD}_\text{offline}$.

In practice

• Implement accumulative risk control.
• Apply nested Pareto set learning.
• Use $\text{IGD}_\text{offline}$ for selection.

Topics

• Multi-objective Optimization
• Offline Optimization
• Pareto Set Learning
• Out-of-Distribution Robustness
• Risk Control
• Solution Diversity

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.