Mixture-Model Preference Learning for Many-Objective Bayesian Optimization
This addresses the challenge of heterogeneous human preferences in many-objective optimization, though it appears incremental as it builds on existing Bayesian optimization methods.
The paper tackles the problem of preference-based many-objective optimization by proposing a Bayesian framework that learns latent preference archetypes, outperforming standard baselines on synthetic and real-world benchmarks.
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.