Socratic Learning

Modern machine learning techniques often use discriminative models that require large amounts of labeled data. Since generating labeled training data sets is expensive, an alternative approach is to use a generative model, which leverages a simple heuristic to weakly label data. Domain experts prefer using generative models because they “tell a story” about their data. Unfortunately, generative models are typically less accurate than discriminative models. Several recent approaches connect the two types of models to exploit their strengths. In this setting, a misspecified generative model can hurt the performance of subsequent discriminative training. To address this issue, we propose a framework called Socratic learning that automatically uses information from the discriminative model to correct generative model misspecification. This process also provides users with interpretable feedback about how to improve their generative model.