Zobrazeno 1 - 10
of 156
pro vyhledávání: '"Feng, Xingdong"'
Autor:
Wang, Caixing, Feng, Xingdong
The random feature (RF) approach is a well-established and efficient tool for scalable kernel methods, but existing literature has primarily focused on kernel ridge regression with random features (KRR-RF), which has limitations in handling heterogen
Externí odkaz:
http://arxiv.org/abs/2408.13591
In recent years, transfer learning has garnered significant attention in the machine learning community. Its ability to leverage knowledge from related studies to improve generalization performance in a target study has made it highly appealing. This
Externí odkaz:
http://arxiv.org/abs/2310.13966
Covariate shift occurs prevalently in practice, where the input distributions of the source and target data are substantially different. Despite its practical importance in various learning problems, most of the existing methods only focus on some sp
Externí odkaz:
http://arxiv.org/abs/2310.08237
We propose a relative entropy gradient sampler (REGS) for sampling from unnormalized distributions. REGS is a particle method that seeks a sequence of simple nonlinear transforms iteratively pushing the initial samples from a reference distribution i
Externí odkaz:
http://arxiv.org/abs/2110.02787
Autor:
Zhang, Tianqi, Wang, Yunshen, Feng, Xingdong, Zuo, Yizhou, Yu, Hannong, Bao, Hong, Jiang, Fan, Jiang, Shan
Publikováno v:
In iScience 20 September 2024 27(9)
Publikováno v:
In Journal of Econometrics January 2024 238(1)
Autor:
Cheng, Chao, Feng, Xingdong
It becomes an interesting problem to identify subgroup structures in data analysis as populations are probably heterogeneous in practice. In this paper, we consider M-estimators together with both concave and pairwise fusion penalties, which can deal
Externí odkaz:
http://arxiv.org/abs/2005.00248
Deep semi-supervised learning has been widely implemented in the real-world due to the rapid development of deep learning. Recently, attention has shifted to the approaches such as Mean-Teacher to penalize the inconsistency between two perturbed inpu
Externí odkaz:
http://arxiv.org/abs/2004.14227
We develop a constructive approach for $\ell_0$-penalized estimation in the sparse accelerated failure time (AFT) model with high-dimensional covariates. Our proposed method is based on Stute's weighted least squares criterion combined with $\ell_0$-
Externí odkaz:
http://arxiv.org/abs/2002.03318
In statistical learning, identifying underlying structures of true target functions based on observed data plays a crucial role to facilitate subsequent modeling and analysis. Unlike most of those existing methods that focus on some specific settings
Externí odkaz:
http://arxiv.org/abs/1901.00615