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Computational design problems arise in a number of settings, from syntheticbiology to computer architectures. In this paper, we aim to solve data-drivenmodel-based optimization (MBO) problems, where the goal is to find a designinput that maximizes an unknown objective function provided access to only astatic dataset of prior experiments. Such data-driven optimization proceduresare the only practical methods in many real-world domains where active datacollection is expensive (e.g., when optimizing over proteins) or dangerous(e.g., when optimizing over aircraft designs). Typical methods for MBO thatoptimize the design against a learned model suffer from distributional shift:it is easy to find a design that "fools" the model into predicting a highvalue. To overcome this, we propose conservative objective models (COMs), amethod that learns a model of the objective function that lower bounds theactual value of the ground-truth objective on out-of-distribution inputs, anduses it for optimization. Structurally, COMs resemble adversarial trainingmethods used to overcome adversarial examples. COMs are simple to implement andoutperform a number of existing methods on a wide range of MBO problems,including optimizing protein sequences, robot morphologies, neural networkweights, and superconducting materials.

Conservative Objective Models for Effective Offline Model-Based Optimization