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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Random feature selection outperforms many state-of-the-art unsupervised feature selection methods on standard performance and efficiency metrics.
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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.
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Worse than Random: The Importance of a Baseline for Unsupervised Feature Selection
Random feature selection outperforms many state-of-the-art unsupervised feature selection methods on standard performance and efficiency metrics.