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stat.ML2026
Highly Adaptive Principal Component Regression
Mingxun Wang, Alejandro Schuler, Mark van der Laan +1
The Highly Adaptive Lasso (HAL) is a nonparametric regression method that achieves almost dimension-free convergence rates under minimal smoothness assumptions, but its implementat…
stat.ML2025
Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer
Toru Shirakawa, Yi Li, Yulun Wu +5
We propose Deep Longitudinal Targeted Minimum Loss-based Estimation (Deep LTMLE), a novel approach to estimate the counterfactual mean of outcome under dynamic treatment policies i…
stat.ML2024
Highly Adaptive Ridge
Alejandro Schuler, Alexander Hagemeister, Mark van der Laan
In this paper we propose the Highly Adaptive Ridge (HAR): a regression method that achieves a dimension-free L2 convergence rate in the class of right-continuous functio…