Mixture-Model Preference Learning for Many-Objective Bayesian Optimization
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918417315921920 |
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| author | Dubey, Manisha De Peuter, Sebastiaan Wang, Wanrong Kaski, Samuel |
| author_facet | Dubey, Manisha De Peuter, Sebastiaan Wang, Wanrong Kaski, Samuel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28410 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Mixture-Model Preference Learning for Many-Objective Bayesian Optimization Dubey, Manisha De Peuter, Sebastiaan Wang, Wanrong Kaski, Samuel Machine Learning 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. |
| title | Mixture-Model Preference Learning for Many-Objective Bayesian Optimization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.28410 |