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of 219
pro vyhledávání: '"Lin, Huazhen"'
Generative adversarial learning with optimal input dimension and its adaptive generator architecture
We investigate the impact of the input dimension on the generalization error in generative adversarial networks (GANs). In particular, we first provide both theoretical and practical evidence to validate the existence of an optimal input dimension (O
Externí odkaz:
http://arxiv.org/abs/2405.03723
This paper aims to conduct a comprehensive theoretical analysis of current diffusion models. We introduce a novel generative learning methodology utilizing the Schr{\"o}dinger bridge diffusion model in latent space as the framework for theoretical ex
Externí odkaz:
http://arxiv.org/abs/2404.13309
Kidney transplantation is the most effective renal replacement therapy for end stage renal disease patients. With the severe shortage of kidney supplies and for the clinical effectiveness of transplantation, patient's life expectancy post transplanta
Externí odkaz:
http://arxiv.org/abs/2310.10048
In this paper, we develop a novel efficient and robust nonparametric regression estimator under a framework of feedforward neural network. There are several interesting characteristics for the proposed estimator. First, the loss function is built upo
Externí odkaz:
http://arxiv.org/abs/2309.12872
Autor:
Wen, Shoudao, Lin, Huazhen
The literature on high-dimensional functional data focuses on either the dependence over time or the correlation among functional variables. In this paper, we propose a factor-guided functional principal component analysis (FaFPCA) method to consider
Externí odkaz:
http://arxiv.org/abs/2211.12012
This paper proposes a general two directional simultaneous inference (TOSI) framework for high-dimensional models with a manifest variable or latent variable structure, for example, high-dimensional mean models, high-dimensional sparse regression mod
Externí odkaz:
http://arxiv.org/abs/2012.11100
We propose a new class of semiparametric regression models of mean residual life for censored outcome data. The models, which enable us to estimate the expected remaining survival time and generalize commonly used mean residual life models, also cond
Externí odkaz:
http://arxiv.org/abs/2011.04067
Akademický článek
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Publikováno v:
Statistica Sinica, 2022 Jan 01. 32(2), 847-868.
Externí odkaz:
https://www.jstor.org/stable/27118799
Autor:
He, Kevin, Kang, Jian, Hong, Hyokyoung Grace, Zhu, Ji, Li, Yanming, Lin, Huazhen, Xu, Han, Li, Yi
Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size. In order to identify more novel biomarkers and understand biological mechanisms, it is vital to detect signals
Externí odkaz:
http://arxiv.org/abs/1805.06595