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 problems. The researchers also developed a related method called residual proximal boosting to improve accuracy. The study shows that proximal boosting outperforms traditional gradient boosting in terms of speed and accuracy when tested on different types of data.