两篇新研究论文探讨了贝叶斯优化（BO）技术的进展，BO是一种用于优化复杂函数的技术。第一篇论文介绍了一种直接遗憾优化方法，该方法联合学习模型和采集函数，在高性能设置下优于标准基线。第二篇论文深入探讨了时变贝叶斯优化（TVBO）的理论理解，为实现渐进无遗憾性能提供了界限和条件，涵盖了各种核函数。 AI
影响 推进了对优化技术至关重要的理论理解和实际性能，这些技术对AI模型训练和超参数调整至关重要。
排序理由 两篇学术论文发表在arXiv上，详细介绍了贝叶斯优化新理论和实践方法。
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arXiv:2610.08969v1 Announce Type: new Abstract: Black-box optimization problems are ubiquitous across science and engineering, often dealing with expensive objective functions. This objective latency has two consequences during optimization: (i) the objective evaluation dominates…
arXiv:2610.10174v1 Announce Type: new Abstract: Bayesian optimization often involves multiple objectives, constraints, and fidelity levels. We address the challenge of jointly selecting where and at which fidelity to evaluate to identify the highest-fidelity feasible Pareto front…
arXiv:2610.10186v1 Announce Type: new Abstract: Bayesian optimization (BO) is widely used as a standard approach for expensive black-box optimization. However, BO algorithms often involve parameters that must be specified in advance, and their performance can strongly depend on t…
arXiv:2602.07144v3 Announce Type: replace Abstract: Bayesian optimization (BO) is a popular technique for sample-efficient optimization of black-box functions. In many applications, the parameters being tuned come with a carefully engineered default configuration, and practitione…
arXiv:2507.06529v2 Announce Type: replace Abstract: Bayesian optimization (BO) is a powerful paradigm for optimizing expensive black-box functions. Traditional BO methods typically rely on separate hand-crafted acquisition functions and surrogate models for the underlying functio…
arXiv:2505.13012v3 Announce Type: replace-cross Abstract: Time-Varying Bayesian Optimization (TVBO) is the go-to framework for optimizing a time-varying black-box objective function that may be noisy and expensive to evaluate, but its excellent empirical performance remains to be…