Data Usage across the Machine Learning Pipeline

Abstract

In this talk, I will give an overview of past and ongoing work in developing abstract interpretation-based static analyses for reasoning about data and input usage across the machine learning development pipeline. I will present work targeting data processing software (Python and Jupyter Notebooks) or trained machine learning models (neural networks but also decision tree ensembles and support vector machines), as well as model training itself.