Recent works have shown that diffusion models can learn essentially any distribution provided one can perform score estimation.

In this section, we first provide a summary of the notation in Table A2.

Matrices are exceptionally useful in various fields of study as they provide a convenient framework to organize and manipulate data in a structured manner.

However, current approaches can only model distributions for which training samples are directly accessible, which is not the case in many real-world tasks.