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Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation — arXiv:2609.13396

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.13396
License (as archived): CC0-1.0
Archived: 2026-10-15T22:46:25.182Z
SHA-256 of the archived text: a77284dd4438e6f09f1cee8bbefc1dfa9dfb2c1bd79611221dc6860ec2006666
Stable URL of this page: https://metraipolis.com/documento/dc_ee90fe035fad

How to cite

Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation — arXiv:2609.13396 — arXiv (en), https://arxiv.org/abs/2609.13396 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ee90fe035fad (Metraipolis archive dc_ee90fe035fad, sha256 a77284dd4438e6f09f1cee8bbefc1dfa9dfb2c1bd79611221dc6860ec2006666)

BibTeX

@misc{dcee90fe035fad,
  title = {Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation — arXiv:2609.13396},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.13396},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ee90fe035fad; sha256 a77284dd4438e6f09f1cee8bbefc1dfa9dfb2c1bd79611221dc6860ec2006666},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.13396. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In MOBO, the goal is to adequately approximate the Pareto front; that is, to obtain a high-quality solution set with 1) good convergence (closeness to the Pareto front) and 2) good diversity (spread across the Pareto front). Existing MOBO methods typically aim to accomplish these two tasks simultaneously, i.e., driving the search towards the Pareto front while maintaining a diverse set of nondominated solutions, such that the solutions, ideally, can gradually approach the entire front. When sufficient search budgets are available, this approach is effective. However, considering both convergence and diversity throughout the search is not easy and requires careful design. Under very tight budgets, there may not be enough solutions generated to be able to simultaneously approach the entire Pareto front. To address this issue, this paper proposes a \textit{converge-then-diversify} (CTD) approach that decouples convergence and diversity into two stages. In the first stage, CTD focuses on convergence, aiming to quickly drive the search toward a single point on the Pareto front. In the second stage, CTD focuses on diversity, aiming to spread solutions across the front. We present two simple instantiations of CTD by using widely adopted acquisition functions in the area. Experimental results show that, across all 446 pairwise comparisons, CTD statistically outperforms state-of-the-art methods in 72.9\% of the cases, performs equivalently in 21.1\%, and is statistically worse in only 6.1\%, with the advantage being particularly evident in settings with very tight evaluation budgets or in high-dimensional problems.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-15 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Machine Learning (stat.ML).
Pagina del lavoro: https://arxiv.org/abs/2609.13396 — 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 2026-10-15T22:29:03.049Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).