Offline Stochastic Optimization of Black-Box Objective Functions
This addresses optimization challenges in fields like drug discovery and network design by improving robustness and data efficiency, though it is incremental.
The paper tackles the problem of optimizing complex black-box functions with stochasticity in offline settings, introducing Stochastic Offline BBO (SOBBO) with solutions for large-data and scarce-data regimes, showing effectiveness in numerical experiments.
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.