RoMA: RobustModelAdaptation forOfflineModel-basedOptimization

Neural Information Processing Systems 

To handle the issue, we propose a new framework, coined robust model adaptation (RoMA), based on gradient-based optimization of inputs over the DNN. Specifically, it consists oftwosteps: (a)apre-training strategytorobustly train theproxy model and (b) a novel adaptation procedure of the proxy model to have robust estimates for aspecific set ofcandidate solutions. Atahigh level, our scheme utilizes thelocal smoothness priorto overcome the brittleness of the DNN.

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