Conflict-Averse Gradient Optimization of Ensembles for Effective Offline Model-Based Optimization

Understand

Data-driven offline model-based optimization (MBO) is an established practical approach to black-box computational design problems for which the true objective function is unknown and expensive to query.

• However, the standard approach which optimizes designs against a learned proxy model of the ground truth objective can suffer from distributional shift.
• Specifically, in high-dimensional design spaces where valid designs lie on a narrow manifold, the standard approach is susceptible to producing out-of-distribution, invalid designs that "fool" the learned proxy model into outputting a high value.
• Using an ensemble rather than a single model as the learned proxy can help mitigate distribution shift, but naive formulations for combining gradient information from the ensemble, such as minimum or mean gradient, are still suboptimal and often hampered by non-convergent behavior.