Learning to Abstain via Curve Optimization

In practical applications of machine learning, it is often desirable to identify and abstain on examples where the a model's predictions are likely to be incorrect. We consider the problem of selecting a budget-constrained subset of test examples to abstain on, with the goal of maximizing performance on the remaining examples. We develop a novel approach to this problem by analytically optimizing the expected marginal improvement in a desired performance metric, such as the area under the ROC curve or Precision-Recall curve. We compare our approach to other abstention techniques for deep learning models based on posterior probability and uncertainty estimates obtained using test-time dropout. On various tasks in computer vision, natural language processing, and bioinformatics, we demonstrate the consistent effectiveness of our approach over other techniques. We also introduce novel diagnostics based on influence functions to understand the behavior of abstention methods in the presence of noisy training data, and leverage the insights to propose a new influence-based abstention method.