发表机构
Nagoya Institute of Technology（名古屋工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出统一信息论框架，通过变分下界衡量信息增益，联合优化多保真多目标贝叶斯优化中的位置与保真度选择，无需启发式处理约束，实验验证其有效性。
AI 中文摘要
贝叶斯优化通常涉及多个目标、约束和保真度级别。我们解决了在此组合设置中联合选择评估位置和保真度以识别最高保真度可行帕累托前沿的挑战。从统一的信息论视角，我们通过观测提供的关于该前沿的信息增益来衡量查询效用。由于该互信息难以处理，我们利用帕累托一致区域的截断上近似和下近似的混合推导出变分下界。多保真代理模型将信息传播到任意保真度，产生一个成本感知的采集函数，无需单独的保真度选择或约束处理启发式方法。在合成、基准和实际问题上进行的实验证明了该方法在多样化目标、约束和保真度设置中的有效性。
英文摘要
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 frontier in this combined setting. From a unified information-theoretic perspective, we measure query utility by the information gain about this frontier, provided by an observation. Since this mutual information is intractable, we derive a variational lower bound using a mixture of under- and over-truncated approximations to the Pareto-consistent region. Multi-fidelity surrogate models propagate the information to arbitrary fidelities, yielding a cost-aware acquisition function without separate heuristics for fidelity selection or constraint handling. Experiments on synthetic, benchmark, and real-world problems demonstrate effectiveness across diverse objective, constraint, and fidelity settings.
