InfoGram and admissible machine learning

DOI10.1007/s10994-021-06121-4OpenAlexW4206658061MaRDI QIDQ2127228

Subhadeep Mukhopadhyay

Publication date: 20 April 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2108.07380

zbMATH Keywords

fairnessinterpretabilityinformation-theoryadmissible machine learningALFA-testingalgorithmic risk managementCOREmlFINEmlInfoGramL-features

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• plsgenomics
• glmnet
• iRF

Cites Work

• Unnamed Item
• Greedy function approximation: A gradient boosting machine.
• Population theory for boosting ensembles.
• Statistical behavior and consistency of classification methods based on convex risk minimization.
• A definition of conditional mutual information for arbitrary ensembles
• Panning for Gold: ‘Model-X’ Knockoffs for High Dimensional Controlled Variable Selection
• The Conditional Permutation Test for Independence While Controlling for Confounders

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