U-Processes and Preference Learning | Citedby Results | Neural Computation | MIT Press

Skip to Main Content

Skip Nav Destination

U-Processes and Preference Learning

Neural Computation (2014) 26 (12): 2896–2924.

This article has been cited by the following articles in journals that are participating in Crossref Cited-by Linking.

Limit theorems for a class of processes generalizing the U -empirical process

Salim Bouzebda
Inass Soukarieh

Stochastics (2024) 96 (1): 799.

Limit theorems for nonparametric conditional U-statistics smoothed by asymmetric kernels

Salim Bouzebda
Amel Nezzal
Issam Elhattab

AIMS Mathematics (2024) 9 (9): 26195.

Concordance and value information criteria for optimal treatment decision

Chengchun Shi
Rui Song
Wenbin Lu

The Annals of Statistics (2021) 49 (1)

Debiased magnitude-preserving ranking: Learning rate and bias characterization

Hong Chen
Yingjie Wang
Biqin Song
Han Li

Journal of Mathematical Analysis and Applications (2020) 486 (2): 123881.

Error bound of magnitude-preserving ranking with Fredholm kernel

Yanfang Tao
Yi Tang
Feng Shi
Chuanbao Ren

Neurocomputing (2018) 273: 296.

Learning rate of magnitude-preserving regularization ranking with dependent samples

Hong Chen

International Journal of Wavelets, Multiresolution and Information Processing (2016) 14 (01): 1650001.

Local Rademacher complexity bounds based on covering numbers

Yunwen Lei
Lixin Ding
Yingzhou Bi

Neurocomputing (2016) 218: 320.

Comments and Correction on ”U-Processes and Preference Learning” (Neural Computation Vol. 26, pp. 2896–2924, 2014)

Wojciech Rejchel
Hong Li
Chuanbao Ren
Luoqing Li

Neural Computation (2015) 27 (7): 1549.

Refined Generalization Bounds of Gradient Learning over Reproducing Kernel Hilbert Spaces

Shao-Gao Lv

Neural Computation (2015) 27 (6): 1294.