5 papers
Wasserstein bounds for denoising diffusion probabilistic models via the Föllmer process
Yuta Koike
This paper studies sampling error bounds for denoising diffusion probabilistic models (DDPMs) in the 2-Wasserstein distance. Our contributions are threefold. (i) Under general Lips…
A note on connections between the Föllmer process and the denoising diffusion probabilistic model
Yuta Koike
The Föllmer process is a Brownian motion conditioned to have a pre-specified distribution at time 1. This process can be interpreted as an "augmented" time-compressed version of t…
High-dimensional bootstrap and asymptotic expansion
Yuta Koike
The recent seminal work of Chernozhukov, Chetverikov and Kato has shown that bootstrap approximation for the maximum of a sum of independent random vectors is justified even when t…
On lead-lag estimation of non-synchronously observed point processes
Takaaki Shiotani, Takaki Hayashi, Yuta Koike
This paper introduces a new theoretical framework for analyzing lead-lag relationships between point processes, with a special focus on applications to high-frequency financial dat…
Gaussian Approximation for High-Dimensional -statistics with Size-Dependent Kernels
Shunsuke Imai, Yuta Koike
Motivated by small bandwidth asymptotics for kernel-based semiparametric estimators in econometrics, this paper establishes Gaussian approximation results for high-dimensional fixe…