mlsanityData to 14 Oct 2026

Contextualized Machine Learning

arXiv 2310.11340 ↗First posted 17 Oct 2023stat.ML cs.LGcited by 4

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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.

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

16 of 35 references.

References · 16

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  2. 2020Neural Additive Models: Interpretable Machine Learning with Neural Netscited by 319
  3. 2016Not Just a Black Box: Learning Important Features Through Propagating Activation Differencescited by 297
  4. 2016Model-Agnostic Interpretability of Machine Learningcited by 219
  5. 2016Generating Visual Explanationscited by 158
  6. 2017Interpretable & Explorable Approximations of Black Box Modelscited by 54
  7. 2019Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processescited by 53
  8. 2019Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Modelscited by 42
  9. 2018Contextual Parameter Generation for Universal Neural Machine Translationcited by 40
  10. 2017Contextual Explanation Networkscited by 21
  11. 2018Identifiability of Nonparametric Mixture Models and Bayes Optimal Clusteringcited by 15
  12. 2019Learning Sample-Specific Models with Low-Rank Personalized Regressioncited by 5
4 more
  1. 2020Dropout as a Regularizer of Interaction Effectscited by 4
  2. 2013Testing for Homogeneity in Mixture Modelscited by 1
  3. 2018Personalized Survival Prediction with Contextual Explanation Networkscited by 1
  4. 2021NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameterscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 1.

4 papers.

  1. 2023Contextual Feature Selection with Conditional Stochastic Gatescited by 7
  2. 2024Toward AI-Driven Digital Organism: Multiscale Foundation Models for Predicting, Simulating and Programming Biology at All Levelscited by 5
  3. 2026Retrieval-Augmented Interpretable Learning: Towards Task-Specific Zero-Shot Models in Healthcarecited by 1
  4. 2024Patient-Specific Models of Treatment Effects Explain Heterogeneity in Tuberculosiscited by 0