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math.NA2025
Fast operator learning for mapping correlations
Yuehaw Khoo, Yuguan Wang, Siyao Yang
We propose a fast, optimization-free method for learning the transition operators of high-dimensional Markov processes. The central idea is to perform a Galerkin projection of the…
math.NA2025
Optimization-Free Diffusion Model -- A Perturbation Theory Approach
Yuehaw Khoo, Mathias Oster, Yifan Peng
Diffusion models have emerged as a powerful framework in generative modeling, typically relying on optimizing neural networks to estimate the score function via forward SDE simulat…
math.NA2025
Initialization and training of matrix product state probabilistic models
Xun Tang, Yuehaw Khoo, Lexing Ying
Modeling probability distributions via the wave function of a quantum state is central to quantum-inspired generative modeling and quantum state tomography (QST). We investigate a…