Like many optimizers, Bayesian optimization often falls short of gaining user trust due to opacity. While attempts have been made to develop human-centric optimizers, they typically assume user knowledge is well-specified and error-free, employing users mainly as supervisors of the optimization process. We relax these assumptions and propose a more balanced human-AI partnership with our Collaborative and Explainable Bayesian Optimization (CoExBO) framework. Instead of explicitly requiring a user to provide a knowledge model, CoExBO employs preference learning to seamlessly integrate human insights into the optimization, resulting in algorithmic suggestions that resonate with user preference. CoExBO explains its candidate selection every iteration to foster trust, empowering users with a clearer grasp of the optimization. Furthermore, CoExBO offers a no-harm guarantee, allowing users to make mistakes; even with extreme adversarial interventions, the algorithm converges asymptotically to a vanilla Bayesian optimization. We validate CoExBO's efficacy through human-AI teaming experiments in lithium-ion battery design, highlighting substantial improvements over conventional methods. Code is available https://github.com/ma921/CoExBO.

翻译：与许多优化器类似，贝叶斯优化常因不透明性而难以获得用户信任。尽管已有研究尝试开发以人为中心的优化器，但它们通常假设用户知识是明确且无误的，并将用户主要用作优化过程的监督者。我们放宽了这些假设，提出了一种更均衡的人机协作方案——协作与可解释贝叶斯优化框架。该框架无需用户显式提供知识模型，而是通过偏好学习将人类见解无缝融入优化过程，从而产生与用户偏好一致的算法建议。每轮迭代中，框架都会解释其候选方案的选择依据以建立信任，使用户更清晰地把握优化过程。此外，框架提供无损保证，允许用户犯错：即使存在极端对抗性干预，算法仍能渐近收敛至标准贝叶斯优化。我们通过在锂离子电池设计中的人机协作实验验证了框架的有效性，结果表明其相较传统方法具有显著优势。代码已开源：https://github.com/ma921/CoExBO。