Shape regularity of local sets is necessary and sufficient for optimal rates in local averaging estimators for Lipschitz regression functions, with k-NN succeeding by construction and random trees failing without geometric correction.
On the pointwise behavior of recursive parti- tioning and its implications for heterogeneous causal effect estimation.arXiv preprint arXiv:2211.10805
3 Pith papers cite this work. Polarity classification is still indexing.
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SMAVE recasts MAVE for SDR as Riemannian optimization on the Stiefel manifold, yielding a stochastic algorithm with almost-sure convergence and improved runtime over OPG and RMAVE.
MinimaxSplit trees and forests minimize maximum child impurity for splits, with oracle inequalities claiming faster excess risk convergence than CART under non-atomicity conditions, plus empirical gains on EEG regression and image denoising.
citing papers explorer
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Revisiting local regression: shape regularity, uniform rates, and the limits of random splits
Shape regularity of local sets is necessary and sufficient for optimal rates in local averaging estimators for Lipschitz regression functions, with k-NN succeeding by construction and random trees failing without geometric correction.
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Riemannian Stochastic Optimization for Sufficient Dimension Reduction
SMAVE recasts MAVE for SDR as Riemannian optimization on the Stiefel manifold, yielding a stochastic algorithm with almost-sure convergence and improved runtime over OPG and RMAVE.
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Stabilizing the Splits through Minimax Decision Trees
MinimaxSplit trees and forests minimize maximum child impurity for splits, with oracle inequalities claiming faster excess risk convergence than CART under non-atomicity conditions, plus empirical gains on EEG regression and image denoising.