SENSITIVITY-INFORMED REGULARIZATION FOR OFFLINE BLACK-BOX OPTIMIZATION

Manh Cuong Dao, Phi Le Nguyen, Thao Nguyen Truong, Trong Nghia Hoang

Keywords: Offline Optimization, Black-Box Optimization

TL;DR: This paper formalizes the concept of model sensitivity and develops a sensitivity-informed regularization framework to boost the performance of existing offline optimization approaches..

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.

Primary Area: general machine learning (i.e., none of the above)

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Submission Number: 7087