Offline Stochastic Optimization of Black-Box Objective Functions

• URL: http://arxiv.org/abs/2412.02089v1
• Date: Tue, 03 Dec 2024 02:20:30 GMT
• Title: Offline Stochastic Optimization of Black-Box Objective Functions
• Authors: Juncheng Dong, Zihao Wu, Hamid Jafarkhani, Ali Pezeshki, Vahid Tarokh,
• Abstract summary: It is essential to leverage existing data to avoid costly active queries of complex black-box functions. We introduce Offline BBO (SOBBO), which tackles both black-box objectives and uncontrolled uncertainties. Numerical experiments demonstrate the effectiveness of our approach on both synthetic and real-world tasks.
• Score: 47.74033738624514
• License:
• Abstract: Many challenges in science and engineering, such as drug discovery and communication network design, involve optimizing complex and expensive black-box functions across vast search spaces. Thus, it is essential to leverage existing data to avoid costly active queries of these black-box functions. To this end, while Offline Black-Box Optimization (BBO) is effective for deterministic problems, it may fall short in capturing the stochasticity of real-world scenarios. To address this, we introduce Stochastic Offline BBO (SOBBO), which tackles both black-box objectives and uncontrolled uncertainties. We propose two solutions: for large-data regimes, a differentiable surrogate allows for gradient-based optimization, while for scarce-data regimes, we directly estimate gradients under conservative field constraints, improving robustness, convergence, and data efficiency. Numerical experiments demonstrate the effectiveness of our approach on both synthetic and real-world tasks.

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