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Simple Extensions of Single-Objective Acquisition Functions and Hedge Strategies for Multi-Objective Bayesian Optimization — arXiv:2609.31940

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.31940
License (as archived): CC0-1.0
Archived: 2026-10-07T22:36:08.376Z
SHA-256 of the archived text: 44b7ffa932dce0e4b59ceac955e57efec770f73005dd9c372a08f4a7d698f36d
Stable URL of this page: https://metraipolis.com/documento/dc_da4e3ad37bb2

How to cite

Simple Extensions of Single-Objective Acquisition Functions and Hedge Strategies for Multi-Objective Bayesian Optimization — arXiv:2609.31940 — arXiv (en), https://arxiv.org/abs/2609.31940 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_da4e3ad37bb2 (Metraipolis archive dc_da4e3ad37bb2, sha256 44b7ffa932dce0e4b59ceac955e57efec770f73005dd9c372a08f4a7d698f36d)

BibTeX

@misc{dcda4e3ad37bb2,
  title = {Simple Extensions of Single-Objective Acquisition Functions and Hedge Strategies for Multi-Objective Bayesian Optimization — arXiv:2609.31940},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.31940},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_da4e3ad37bb2; sha256 44b7ffa932dce0e4b59ceac955e57efec770f73005dd9c372a08f4a7d698f36d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.31940. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Multi-objective Bayesian optimization (MOBO) is commonly approached through specialized acquisition functions or scalarization schemes designed to explicitly account for trade-offs among non-preferential objectives. In this work, we show that such complexity might be unnecessary. We propose a framework that extends standard single-objective acquisition functions directly to the multi-objective setting through a hypervolume-based transformation. We further extend hedge strategies for acquisition functions, which are typically used only in single-objective optimization, to the multi-objective regime. Our approach requires minimal modification to existing Bayesian optimization pipelines and avoids the need for bespoke multi-objective formulations. We demonstrate how a broad class of commonly used single-objective acquisition functions and hedge strategies can be adapted in a principled manner to handle multiple objectives, while preserving their intuitive interpretation and computational efficiency. Empirically, we evaluate the proposed methods across a range of synthetic and real-world multi-objective benchmarks. Despite their simplicity, our extensions consistently match or outperform more complex state-of-the-art MOBO methods in terms of optimization performance and sample efficiency. These results suggest that effective multi-objective Bayesian optimization can be achieved by reusing and carefully extending well-established single-objective acquisition strategies, offering a simpler and more flexible alternative to existing approaches.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG) ; Optimization and Control (math.OC); Machine Learning (stat.ML).
Pagina del lavoro: https://arxiv.org/abs/2609.31940 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T22:14:27.555Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).