A conditional adaptive perturbation approach enables valid in-sample inference for machine learning-identified subgroups with nonregular boundaries via triple robustness.
Advances in neural information processing systems , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
FedAvg DeepSurv across Lifelines (n=148k, self-report) and Rotterdam Study (n=10k, linked outcomes) raised C-statistics from 0.728 to 0.739 and 0.783 to 0.787 versus local training.
citing papers explorer
-
In-Sample Evaluation of Subgroups Identified by Generic Machine Learning
A conditional adaptive perturbation approach enables valid in-sample inference for machine learning-identified subgroups with nonregular boundaries via triple robustness.
-
Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction
FedAvg DeepSurv across Lifelines (n=148k, self-report) and Rotterdam Study (n=10k, linked outcomes) raised C-statistics from 0.728 to 0.739 and 0.783 to 0.787 versus local training.