Gradient Regularized Newton Boosting Trees with Global Convergence
Gradient Boosting Decision Trees (GBDTs) dominate tabular machine learning, with modern implementations like XGBoost, LightGBM, and CatBoost being based on Newton boosting: a second-order descent step in the space of decision trees. Despite its empirical success, the global convergence of Newton boosting is poorly understood compared to first-order boosting. In this paper, we introduce Restricted Newton Descent, which studies convex optimization with Newton's method on Hilbert spaces with inexact iterates, based on the concepts of cosine angle and weak gradient edge. Within this framework, we recover Newton boosting with GBDTs and classical finite-dimensional theory as special cases. We f...
| Authors | Nikita Zozoulenko, Daniel Falkowski, Thomas Cass et al. |
|---|---|
| Primary category | stat.ML |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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