Preference-based many-objective optimization faces two obstacles: an expanding space of trade-offs and heterogeneous, context-dependent human value structures. Towards this, we propose a Bayesian framework that learns a small set of latent preference archetypes rather than assuming a single fixed utility function, modelling them as components of a Dirichlet-process mixture with uncertainty over both archetypes and their weights. To query efficiently, we designing hybrid queries that target information about (i) mode identity and (ii) within-mode trade-offs. Under mild assumptions, we provide a simple regret guarantee for the resulting mixture-aware Bayesian optimization procedure. Empirically, our method outperforms standard baselines on synthetic and real-world many-objective benchmarks, and mixture-aware diagnostics reveal structure that regret alone fails to capture.

翻译：摘要：基于偏好的多目标优化面临两大障碍：不断扩展的权衡空间以及异质且依赖于上下文的人类价值结构。针对这一问题，我们提出一种贝叶斯框架，该框架学习少量潜在偏好原型，而非假设单一固定效用函数；这些原型被建模为狄利克雷过程混合模型的组成部分，其中对原型及其权重均存在不确定性。为实现高效查询，我们设计了一种混合查询策略，同时针对（i）模式身份与（ii）模式内权衡两类信息进行采样。在温和假设下，我们为所得混合感知贝叶斯优化过程提供了简单遗憾保证。实验表明，该方法在合成与真实多目标基准测试中均优于标准基线，且混合感知诊断揭示了单纯优化遗憾值无法捕捉的结构信息。