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stat.ML2020★ 6 cited
Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners
Elias Chaibub Neto
Linear residualization is a common practice for confounding adjustment in machine learning (ML) applications. Recently, causality-aware predictive modeling has been proposed as an…
stat.ML2020★ 1 cited
Stable predictions for health related anticausal prediction tasks affected by selection biases: the need to deconfound the test set features
Elias Chaibub Neto, Phil Snyder, Solveig K Sieberts +1
In health related machine learning applications, the training data often corresponds to a non-representative sample from the target populations where the learners will be deployed.…
stat.ML2018
Detecting Learning vs Memorization in Deep Neural Networks using Shared Structure Validation Sets
Elias Chaibub Neto
The roles played by learning and memorization represent an important topic in deep learning research. Recent work on this subject has shown that the optimization behavior of DNNs t…