Offline black-box optimization aims to identify the optimal solution of a black-box objective function under the guidance of a surrogate model constructed solely from a pre-collected dataset. It is commonly used in industrial scenarios, which often involve constraints, i.e., constrained offline optimization (COO). Offline optimization has progressed in addressing the out-of-distribution (OOD) issue caused by its inherent inability to interact with the objective function. However, there is not enough research in addressing more difficult scenarios, which must simultaneously address OOD issues and constrained issues to find stable, high-quality (i.e., high-scoring and feasible) solutions. To bridge this gap, this paper proposes a method called constrained offline optimization via risk evaluation and management (COOREM), which is capable of consistently surpassing the offline dataset under the condition of satisfying constraints. Specifically, COOREM employs a dual-energy model to separately evaluate OOD risk and constrained risk. This separation strategy aims to distinguish and address two difficult cases: the infeasible but not OOD solutions and the feasible but OOD solutions. Moreover, COOREM effectively manages OOD risk and constrained risk, ensuring the identification of high-quality solutions. Extensive experiments on real-world tasks, e.g., space missions, process synthesis, and design problems, showcase COOREM's effectiveness in managing both OOD risk and constrained risk. Furthermore, our findings indicate that COOREM could outperform online methods that need to access the objective function in certain space missions.

Black-box optimization aims to find the optima through building a model close to the black-box objective function based on function value evaluation. However, in many real-world tasks, such as the design of molecular formulas and mechanical structures, it is perilous, costly, or even infeasible to evaluate the objective function value of an actively sampled solution. In this situation, optimization can only be conducted via utilizing offline historical data, which yields offline black-box optimization. Different from the traditional goal that is to pursue the optimal solution, this paper emphasizes that the goal of offline optimization is to stably surpass the offline dataset during optimization procedure. Although benchmarks called Design-Bench already exist in this emerging field, it can hardly evaluate the stability of offline optimization and mainly provides real-world offline tasks and the corresponding offline datasets. To this end, this paper proposes benchmarks named SOO-Bench (i.e., Stable Offline Optimization Benchmarks) for offline black-box optimization algorithms, so as to systematically evaluate the stability of surpassing the offline dataset under different data distributions. Along with SOO-Bench, we also propose a stability indicator to measure the degree of stability. Specifically, SOO-Bench includes various real-world offline optimization tasks and offline datasets under different data distributions, involving the fields of satellites, materials science, structural mechanics, and automobile manufacturing. Empirically, baseline and state-of-the-art algorithms are tested and analyzed on SOO-Bench. Hopefully, SOO-Bench is expected to serve as a catalyst for the rapid developments of more novel and stable offline optimization methods. The code is available at \url{https://github.com/zhuyiyi-123/SOO-Bench}.

Customer segmentation plays a crucial role in credit risk assessment by dividing users into specific risk levels based on their credit scores. Previous methods fail to comprehensively consider the stability in the segmentation process, resulting in frequent changes and inconsistencies in users' risk levels over time. This increases potential risks to a company. To this end, this paper at first introduces and formalizes the concept of stability regret in the segmentation process. However, evaluating stability is challenging due to its black-box nature and the computational burden posed by vast user data sets. To address these challenges, this paper proposes a large-scale stable customer segmentation approach named LASCA. LASCA consists of two phases: high-quality dataset construction (HDC) and reliable data-driven optimization (RDO). Specifically, HDC utilizes an evolutionary algorithm to collect high-quality binning solutions. RDO subsequently builds a reliable surrogate model to search for the most stable binning solution based on the collected dataset. Extensive experiments conducted on real-world large-scale datasets (up to 0.8 billion) show that LASCA surpasses the state-of-the-art binning methods in finding the most stable binning solution. Notably, HDC greatly enhances data quality by 50%. RDO efficiently discovers more stable binning solutions with a 36% improvement in stability, accelerating the optimization process by 25 times via data-driven evaluation. Currently, LASCA has been successfully deployed in the large-scale credit risk assessment system of Alipay.

Offline optimization aims to elaborately construct a solution that optimizes a black-box function with only access to the offline dataset. A typical manner of constructing the solution is to train a surrogate model of the black-box function on the offline dataset and optimize the solution guided by the surrogate model. However, this manner often encounters a fundamental challenge that the surrogate model could erroneously estimate out-of-distribution (OOD) solutions. Therefore, the optimizer would be misled to produce inferior solutions for online applications, i.e., degradation of performance. To this end, this paper formalizes the risk of degradation for OOD solutions and proposes an accumulative risk controlled offline optimization (ARCOO) method. Specifically, ARCOO learns a surrogate model in conjunction with an energy model. The energy model characterizes the risk of degradation by learning on high-risk solutions and low-risk ones contrastively. In the optimization procedure, the behavior of the optimizer in each step is controlled by a risk suppression factor calculated via the energy model, which leads to the controllable accumulative risk. Theoretically, we justify the efficacy of energy for accumulative risk control. Extensive experiments on offline optimization tasks show that ARCOO surpasses state-of-the-art methods in both degradation-resistance and optimality of the output solution.