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SGLB: Stochastic Gradient Langevin Boosting
P. 1–10.
Ustimenko A., Prokhorenkova L.
This paper introduces Stochastic Gradient Langevin Boosting (SGLB) - a powerful and efficient machine learning framework that may deal with a wide range of loss functions and has provable generalization guarantees. The method is based on a special form of the Langevin diffusion equation specifically designed for gradient boosting. This allows us to theoretically guarantee the global convergence even for multimodal loss functions, while standard gradient boosting algorithms can guarantee only local optimum. We also empirically show that SGLB outperforms classic gradient boosting when applied to classification tasks with 0-1 loss function, which is known to be multimodal.
Language:
English
In book
Vol. 139. , PMLR, 2021.
Nikita I. Lysenok, International Journal of Computer Information Systems and Industrial Management Applications 2026 Vol. 18 No. 17s P. 533–549
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Added: August 2, 2021
Ivanov S., Prokhorenkova L., , in: Proceedings of the 9th International Conference on Learning Representations (ICLR 2021). ICLR, 2021.: ICLR, 2021..
Added: August 2, 2021
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Added: January 14, 2021
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Added: January 20, 2020
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Added: January 19, 2019