Discrete Diffusion Models are Minimax Optimal Under Sparsity
Discrete diffusion models have emerged as a leading alternative to autoregressive models for generating text and other categorical data, yet their theoretical foundations remain less developed than those of their continuous counterparts. For continuous diffusion models, a prominent line of theory suggests that these models can adapt to low-dimensional structure in the data, thereby improving statistical efficiency and generalization. In this work, we investigate the discrete analogue of this phenomenon by studying the statistical rates of discrete diffusion models under sparsity assumptions. By analyzing a nonparametric estimator derived from a regularized score-entropy objective, we esta...
| Authors | Luca Blunda, Tyler Farghly, Umut Simsekli et al. |
|---|---|
| Primary category | stat.ML |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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