A projected gradient descent algorithm for noisy inductive matrix completion achieves linear convergence and stable recovery at sample complexity governed by side-information dimension, extending to inexact side-information with optimal error degradation.
Statistical methods in medical research , volume=
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A conditional adaptive perturbation approach enables valid in-sample inference for machine learning-identified subgroups with nonregular boundaries via triple robustness.
The work gives conditions favoring complete-case over IPW estimators in federated settings with missing data and introduces a multi-model calibrated weighting estimator that is consistent when at least one candidate model is correct at each site.
citing papers explorer
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Sample-efficient inductive matrix completion with noise and inexact side-information
A projected gradient descent algorithm for noisy inductive matrix completion achieves linear convergence and stable recovery at sample complexity governed by side-information dimension, extending to inexact side-information with optimal error degradation.
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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.
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Federated Learning with Incomplete Data: When to Use Complete Cases and When to Weight
The work gives conditions favoring complete-case over IPW estimators in federated settings with missing data and introduces a multi-model calibrated weighting estimator that is consistent when at least one candidate model is correct at each site.