# Contextualized Machine Learning

arXiv: https://arxiv.org/abs/2310.11340 · HTML: https://mlsanity.com/paper/2310.11340/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 17 Oct 2023.

## Builds on (most cited first, 16 of 16)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Neural Additive Models: Interpretable Machine Learning with Neural Nets](https://mlsanity.com/paper/2004.13912/) (2020)
- [Not Just a Black Box: Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1605.01713/) (2016)
- [Model-Agnostic Interpretability of Machine Learning](https://mlsanity.com/paper/1606.05386/) (2016)
- [Generating Visual Explanations](https://mlsanity.com/paper/1603.08507/) (2016)
- [Interpretable & Explorable Approximations of Black Box Models](https://mlsanity.com/paper/1707.01154/) (2017)
- [Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes](https://mlsanity.com/paper/1904.09612/) (2019)
- [Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models](https://mlsanity.com/paper/1911.04974/) (2019)
- [Contextual Parameter Generation for Universal Neural Machine Translation](https://mlsanity.com/paper/1808.08493/) (2018)
- [Contextual Explanation Networks](https://mlsanity.com/paper/1705.10301/) (2017)
- [Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering](https://mlsanity.com/paper/1802.04397/) (2018)
- [Learning Sample-Specific Models with Low-Rank Personalized Regression](https://mlsanity.com/paper/1910.06939/) (2019)
- [Dropout as a Regularizer of Interaction Effects](https://mlsanity.com/paper/2007.00823/) (2020)
- [Testing for Homogeneity in Mixture Models](https://mlsanity.com/paper/1302.1805/) (2013)
- [Personalized Survival Prediction with Contextual Explanation Networks](https://mlsanity.com/paper/1801.09810/) (2018)
- [NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters](https://mlsanity.com/paper/2111.01104/) (2021)

## Built on by (most cited first, 4 of 4)

- [Contextual Feature Selection with Conditional Stochastic Gates](https://mlsanity.com/paper/2312.14254/) (2023)
- [Toward AI-Driven Digital Organism: Multiscale Foundation Models for Predicting, Simulating and Programming Biology at All Levels](https://mlsanity.com/paper/2412.06993/) (2024)
- [Retrieval-Augmented Interpretable Learning: Towards Task-Specific Zero-Shot Models in Healthcare](https://mlsanity.com/paper/2607.17508/) (2026)
- [Patient-Specific Models of Treatment Effects Explain Heterogeneity in Tuberculosis](https://mlsanity.com/paper/2411.10645/) (2024)

## Abstract (arXiv)

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.