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arXiv · 2412.02089

Offline Stochastic Optimization of Black-Box Objective Functions

Juncheng Dong·Zihao Wu·Hamid Jafarkhani·Ali Pezeshki·Vahid Tarokh
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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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Juncheng Dong, Zihao Wu, Hamid Jafarkhani, Ali Pezeshki, Vahid Tarokh. 2024-12-03. Offline Stochastic Optimization of Black-Box Objective Functions. https://arxiv.org/abs/2412.02089

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