Minimax Optimality of Score-based Diffusion Models: Beyond the Density Lower Bound Assumptions
<|reference_start|>Minimax Optimality of Score-based Diffusion Models: Beyond the Density Lower Bound Assumptions: We study the asymptotic error of score-based diffusion model sampling in large-sample scenarios from a non-parametric statistics perspective. We show that a kernel-based score estimator achieves an optimal mean square error of $\widetilde{O}\left(n^{-1} t^{-\frac{d+2}{2}}(t^{\frac{d}{2}} \vee 1)\right)$ for the score function of $p_0*\mathcal{N}(0,t\boldsymbol{I}_d)$, where $n$ and $d$ represent the sample size and the dimension, $t$ is bounded above and below by polynomials of $n$, and $p_0$ is an arbitrary sub-Gaussian distribution. As a consequence, this yields an $\widetilde{O}\left(n^{-1/2} t^{-\frac{d}{4}}\right)$ upper bound for the total variation error of the distribution of the sample generated by the diffusion model under a mere sub-Gaussian assumption. If in addition, $p_0$ belongs to the nonparametric family of the $\beta$-Sobolev space with $\beta\le 2$, by adopting an early stopping strategy, we obtain that the diffusion model is nearly (up to log factors) minimax optimal. This removes the crucial lower bound assumption on $p_0$ in previous proofs of the minimax optimality of the diffusion model for nonparametric families.<|reference_end|>
@article{zhang2024minimax,
    title={Minimax Optimality of Score-based Diffusion Models: Beyond the Density
  Lower Bound Assumptions},
    author={Kaihong Zhang, Caitlyn H. Yin, Feng Liang, Jingbo Liu},
    journal={Proceedings of the 41st International Conference on Machine
  Learning, PMLR 235:60134-60178, 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15602},
    primaryClass={math.ST cs.IT cs.LG math.IT stat.ML stat.TH}
}