Towards Building Resilient Ensembles against Training Data Faults

Abstract

In this talk, we describe our approach to construct resilient ML ensembles against training data faults [1]. First, we demonstrate how ensembles tolerate faulty training data. Then, we show how we could use analytical modelling to help ML practitioners build resilient ensembles without the need for resource intensive fault injection experiments.