2 citations · 4 across the 6 of their papers we have counts for
6 papers
On the Robustness of Kernel Ridge Regression Using the Cauchy Loss Function
Hongwei Wen, Annika Betken, Wouter Koolen
Robust regression aims to develop methods for estimating an unknown regression function in the presence of outliers, heavy-tailed distributions, or contaminated data, which can sev…
Median of Forests for Robust Density Estimation
Hongwei Wen, Annika Betken, Tao Huang
Robust density estimation refers to the consistent estimation of the density function even when the data is contaminated by outliers. We find that existing forest density estimatio…
Class Probability Matching Using Kernel Methods for Label Shift Adaptation
Hongwei Wen, Annika Betken, Hanyuan Hang
In domain adaptation, covariate shift and label shift problems are two distinct and complementary tasks. In covariate shift adaptation where the differences in data distribution ar…
Leveraged Weighted Loss for Partial Label Learning
Hongwei Wen, Jingyi Cui, Hanyuan Hang +3
As an important branch of weakly supervised learning, partial label learning deals with data where each instance is assigned with a set of candidate labels, whereas only one of the…
Histogram Transform Ensembles for Large-scale Regression
Hanyuan Hang, Zhouchen Lin, Xiaoyu Liu +1
We propose a novel algorithm for large-scale regression problems named histogram transform ensembles (HTE), composed of random rotations, stretchings, and translations. First of al…
Best-scored Random Forest Density Estimation
Hanyuan Hang, Hongwei Wen
This paper presents a brand new nonparametric density estimation strategy named the best-scored random forest density estimation whose effectiveness is supported by both solid theo…