Sample complexity bounds for categorical Markov random fields via Discrete Diffusions
Many applications in statistics, economics, and physics require sampling from high-dimensional categorical distributions with local dependence structures. Examples include finite memory language models, Ising and Potts systems in statistical physics and protein folding, etc. In modern machine learning, discrete diffusions have emerged as a flexible approach for sampling such data, with strong empirical performance. Motivated by this, we develop learning methods with end-to-end sample complexity bounds for discrete diffusion with uniform noising under local dependence, which we model through low order Markov random fields (MRFs). Our main technical insight is a new \emph{pinning decomposit...
| Authors | Shivam Kumar, Nabarun Deb |
|---|---|
| Primary category | math.ST |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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