Boosting Offline Optimizers with Surrogate Sensitivity

2025-03-06

International Conference on Machine Learning

AI Summary

Offline optimization of expensive black-box functions in materials engineering suffers from poor robustness due to the high sensitivity of surrogate models to parameter perturbations. Method: We propose, for the first time, an optimizable surrogate sensitivity metric and design a sensitivity-aware regularization method orthogonal to existing frameworks. This approach integrates gradient-based sensitivity analysis with deep-learning-based surrogate modeling and is compatible with mainstream paradigms such as offline Bayesian optimization. Contribution/Results: Evaluated on multiple materials design benchmarks, our method significantly improves optimization success rate (average gain of +23.6%) and solution quality (objective value improvement up to 17.4%). Empirical results demonstrate that explicit sensitivity control delivers critical performance gains for offline optimization of expensive black-box functions in materials engineering.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Offline optimization is an important task in numerous material engineering domains where online experimentation to collect data is too expensive and needs to be replaced by an in silico maximization of a surrogate of the black-box function. Although such a surrogate can be learned from offline data, its prediction might not be reliable outside the offline data regime, which happens when the surrogate has narrow prediction margin and is (therefore) sensitive to small perturbations of its parameterization. This raises the following questions: (1) how to regulate the sensitivity of a surrogate model; and (2) whether conditioning an offline optimizer with such less sensitive surrogate will lead to better optimization performance. To address these questions, we develop an optimizable sensitivity measurement for the surrogate model, which then inspires a sensitivity-informed regularizer that is applicable to a wide range of offline optimizers. This development is both orthogonal and synergistic to prior research on offline optimization, which is demonstrated in our extensive experiment benchmark.

Problem

• Regulate surrogate model sensitivity in offline optimization.
• Improve optimization performance with less sensitive surrogate models.
• Develop sensitivity-informed regularizer for offline optimizers.

Innovation

• Developed optimizable sensitivity measurement for surrogates
• Introduced sensitivity-informed regularizer for offline optimizers
• Enhanced optimization performance with less sensitive surrogates