Thinking Beyond Distributions in Testing Machine Learned Models
Thinking Beyond Distributions in Testing Machine Learned Models
Testing practices within the machine learning (ML) community have centered around assessing a learned model's predictive performance measured against a test dataset, often drawn from the same distribution as the training dataset. While recent work on robustness and fairness testing within the ML community has pointed to the importance of …