Computer Science
2 years ago

Boosting Algorithms Revolutionize Predictive Modeling, Outperform Gradient Descent

arXiv (Cornell University)
Proximal boosting: aggregating weak learners to minimize non-differentiable losses
Erwan Fouillen, Claire Boyer, Maxime Sangnier

Paper Summary

Related papers

Theme background image
Computer Science
2 years ago

Boosting Algorithms Revolutionize Predictive Modeling, Outperform Gradient Descent

Transfer Learning
Extreme Learning Machines
Compressed Sensing
1 view
Theme background image
Engineering
2 years ago

New boosting method outperforms gradient boosting in accuracy and speed

Transfer Learning
Compressed Sensing
Optimization Methods
1 view
Theme background image
Computer Science
2 years ago

Newton boosting trumps gradient boosting for better predictive accuracy in data science.

Learning with Noisy Labels
Bayesian Networks
Explainable AI
1 view

Gradient boosting is a method that combines simple models to make accurate predictions. A new approach called proximal boosting is introduced in this paper, which uses a special algorithm to handle non-differentiable data. The researchers show that proximal boosting outperforms traditional gradient boosting in terms of speed and accuracy when tested on both simulated and real data.