Lipschitz functions decompose into monotonic plus linear parts, yielding sample-split estimators with convergence guarantees under heteroscedastic/heavy-tailed errors and adaptivity to unknown function complexity.
01, and we choose the maximum depth of each tree from the set {2, 5}
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From Isotonic to Lipschitz Regression: A New Interpolative Perspective on Shape-restricted Estimation
Lipschitz functions decompose into monotonic plus linear parts, yielding sample-split estimators with convergence guarantees under heteroscedastic/heavy-tailed errors and adaptivity to unknown function complexity.