  An analytic process is iterative between two agents, an analyst and an
analytic toolbox. Each iteration comprises three main steps: preparing a
dataset, running an analytic tool, and evaluating the result, where dataset
preparation and result evaluation, conducted by the analyst, are largely
domain-knowledge driven. In this work, the focus is on automating the result
evaluation step. The underlying problem is to identify plots that are deemed
interesting by an analyst. We propose a methodology to learn such analyst's
intent based on Generative Adversarial Networks (GANs) and demonstrate its
applications in the context of production yield optimization using data
collected from several product lines.
