Title: Poisson Process for Bayesian Optimization. Abstract: Bayesian Optimization (BO) is a sample-efficient, model-based method for optimizing black-box functions which can be expensive to evaluate. Traditionally, BO seeks a probabilistic surrogate model, such as Tree-structured Parzen Estimator (TPE), Sequential Model Algorithm Configuration (SMAC), and Gaussian process (GP), based on the exact observed values. However, compared to the value response, relative ranking is hard to be disrupted due to noise resulting in better robustness. Moreover, it has better practicality when the exact value responses are intractable, but information about candidate preferences can be acquired. Thus, this work introduces an efficient BO framework, named PoPBO, consisting of a novel ranking-based response surface based on Poisson process and two acquisition functions to accommodate the proposed surrogate model. We show empirically that PoPBO improves efficacy and efficiency on both simulated and real-world benchmarks, including HPO and NAS.