A novel risk-averse training framework that leverages score-based generative models for data augmentation tailored to Conditional Value-at-Risk minimization

We prove algorithm- and data-dependent upper bounds on the generalization error of diffusion models by using tools from statistical learning theory

We study the approximation and generalization abilities of score-based neural network generative models