A new adaptive ℓ₂-regularized Newton boosting algorithm for decision trees delivers global O(1/k²) convergence on general convex losses, recovering classical Newton boosting as a special case under stronger assumptions.
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Gradient Regularized Newton Boosting Trees with Global Convergence
A new adaptive ℓ₂-regularized Newton boosting algorithm for decision trees delivers global O(1/k²) convergence on general convex losses, recovering classical Newton boosting as a special case under stronger assumptions.