Contextualized Machine Learning
Benjamin Lengerich Caleb N. Ellington Andrea Rubbi Manolis Kellis Eric P. Xing
We examine Contextualized Machine Learning (ML), a paradigm for learning
heterogeneous and context-dependent effects. Contextualized ML estimates
heterogeneous functions by applying deep learning to the meta-relationship
between contextual information and context-specific parametric models. This is
a form of varying-coefficient modeling that unifies existing frameworks
including cluster analysis and cohort modeling by introducing two reusable
concepts: a context encoder which translates sample context into model
parameters, and sample-specific model which operates on sample predictors. We
review the process of developing contextualized models, nonparametric inference
from contextualized models, and identifiability conditions of contextualized
models. Finally, we present the open-source PyTorch package ContextualizedML.