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.
Journal of Machine Learning Research , year =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
citing papers explorer
-
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.
-
Soft-MSM: Differentiable Context-Aware Elastic Alignment for Time Series
Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.