Reproduce “Accelerated Gradient Boosting” on OpenResearch
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About this paper
Gérard Biau, Benoît Cadre, Laurent Rouvìère
This research introduces Accelerated Gradient Boosting (AGB), an algorithm that integrates Nesterov's accelerated gradient descent into the gradient boosting framework. AGB achieves predictive performance comparable to standard gradient boosting while significantly reducing the number of required component trees and exhibiting less sensitivity to the shrinkage parameter.
Key insights
- Nesterov's acceleration mechanism, typically used in convex optimization, can be effectively adapted to the functional gradient descent setting of boosting.
- The dual functional components (F_t and G_t) and specific update rules allow for significantly faster convergence to an optimal solution.
- Achieving comparable predictive accuracy with fewer trees demonstrates that acceleration can improve the efficiency of extracting predictive power from ensemble models.
Summary by alphaXiv.
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