7 papers
EMPURPLE: A Free Lunch for Diffusion Distillation based on the Information Bottleneck
Zilai Li, Lujia Bai
Diffusion models achieve impressive image-generation quality but remain expensive at inference time. Diffusion distillation reduces sampling steps, yet many distilled models, inclu…
A portmanteau test for multivariate non-stationary functional time series with an increasing number of lags
Lujia Bai, Holger Dette, Weichi Wu
Multivariate locally stationary functional time series provide a flexible framework for modeling functional data exhibiting both temporal and spatial dependencies while allowing fo…
Complex trend inference for high-dimensional piecewise locally stationary time series
Lujia Bai, David Veitch, Weichi Wu +2
This paper studies high-dimensional trend inference for piecewise smooth signals under nonstationary noise and asynchronous structural breaks by first detecting asynchronous change…
Validating spatial-temporal separability for stationary processes
Lujia Bai, Holger Dette, Zihao Yuan
A crucial assumption to reduce computational complexity in spatial-temporal data analysis is separability, which factors the covariance structure into a purely spatial and a purely…
F-scheduler: illuminating the free-lunch design space for fast sampling of diffusion models
Zilai Li, Lujia Bai
Diffusion models are the state-of-the-art generative models for high-resolution images, but sampling from pretrained models is computationally expensive, motivating interest in fas…
Measuring deviations from spherical symmetry
Lujia Bai, Holger Dette
Most of the work on checking spherical symmetry assumptions on the distribution of the -dimensional random vector has its focus on statistical tests for the null hypothesis…