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New research explores composite modeling for multi-objective Bayesian optimization

By PulseAugur Editorial · [1 sources] ·

This paper explores the benefits of composite modeling within multi-objective Bayesian optimization (MOBO) for scientific design. The research investigates how learning from intermediate responses, rather than just final objective values, can improve sampling efficiency. Experiments on synthetic and application-inspired benchmarks suggest that composite modeling is most effective when the mapping from intermediate responses to final objectives is non-bijective and non-affine. AI

IMPACT This research could lead to more efficient scientific design processes by improving how optimization algorithms learn from experimental data.

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research explores composite modeling for multi-objective Bayesian optimization

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The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ricky Ye, Sahas Goli, Muhammad Ali, Poompol Buathong, Peter I. Frazier ·

    Investigating Composite Modeling in Multi-Objective Bayesian Optimization for Scientific Design

    arXiv:2611.13565v1 Announce Type: new Abstract: Scientific design often requires balancing competing objectives that are expensive to evaluate through experiments or simulations. Multi-objective Bayesian optimization (MOBO) addresses this challenge by using probabilistic models t…

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