AI RESEARCH
Highly Adaptive Principal Component Regression
arXiv CS.LG
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ArXi:2602.10613v2 Announce Type: replace-cross The Highly Adaptive Lasso (HAL) is a nonparametric regression method that achieves almost dimension-free convergence rates under minimal smoothness assumptions, but its implementation can be computationally prohibitive in high dimensions due to the large design matrix it requires. The Highly Adaptive Ridge (HAR) has been proposed as a related ridge-regularized analogue. Building on both procedures, we