The M-survival learner estimates heterogeneous indirect treatment effects in censored survival data and supplies a new criterion to detect mediation heterogeneity for surrogate endpoint validation.
Regularization in kernel learning
2 Pith papers cite this work. Polarity classification is still indexing.
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A random-projection differentially private kernel ERM method attains minimax-optimal excess risk bounds for squared and Lipschitz-smooth convex losses under local strong convexity, plus the first dimension-free bounds for objective-perturbation private linear ERM.
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Modeling Heterogeneous Mediation Effects in Survival Analysis via an Interpretable M-Learner Framework
The M-survival learner estimates heterogeneous indirect treatment effects in censored survival data and supplies a new criterion to detect mediation heterogeneity for surrogate endpoint validation.
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Optimal differentially private kernel learning with random projection
A random-projection differentially private kernel ERM method attains minimax-optimal excess risk bounds for squared and Lipschitz-smooth convex losses under local strong convexity, plus the first dimension-free bounds for objective-perturbation private linear ERM.