Ya-Ting Liu

Learning Transition Kernels of Jump-Diffusion Processes with Conditional Diffusion Models

This work studies the problem of learning transition kernels for time-homogeneous jump-diffusion processes using conditional diffusion models and establishes non-asymptotic bounds for the conditional score estimation error and for the KL divergence between the laws of the true and generated discretely observed paths.