Yinuo Ren

Yinuo Ren is a Ph.D. candidate in Applied and Computational Mathematics at Stanford University’s Institute for Computational and Mathematical Engineering, advised by Lexing Ying and Grant Rotskoff. His research focuses on the mathematical foundations and algorithmic design of generative models, including diffusion and flow-based methods.

Denoising Markov Models Generalize Diffusion Through Reverse-Time Generators

Stanford Ph.D. candidate Yinuo Ren argues that diffusion, discrete diffusion, and broader jump-based generative models can be treated as instances of the same problem: choose a forward Markov process that carries data toward a simple reference law, then learn its reverse-time generator. His framework gives conditions under which that reverse generator is explicit up to unknown densities and turns the resulting approximation problem into a path-space KL objective via Doob’s h-transform. The payoff, Ren says, is a principled way to design denoising models beyond Gaussian diffusion, including discrete and Lévy-type dynamics.

Microsoft ResearchMay 26, 202615 min read