7 citations · 32 across the 21 of their papers we have counts for
21 papers
A Lightweight, Transferable, and Self-Adaptive Framework for Intelligent DC Arc-Fault Detection in Photovoltaic Systems
Xiaoke Yang, Long Gao, Haoyu He +5
Arc-fault circuit interrupters (AFCIs) are essential for mitigating fire hazards in residential photovoltaic (PV) systems, yet achieving reliable DC arc-fault detection under real-…
A Self-Evolving Defect Detection Framework for Industrial Photovoltaic Systems
Haoyu He, Yu Duan, Wenzhen Liu +5
Reliable photovoltaic (PV) power generation requires timely detection of module defects that may reduce energy yield, accelerate degradation, and increase lifecycle operation and m…
Transfer Learning under Covariate Shift: Local -Nearest Neighbours Regression with Heavy-Tailed Design
Petr Zamolodtchikov, Hanyuan Hang
Covariate shift is a common transfer learning scenario where the marginal distributions of input variables vary between source and target data while the conditional distribution of…
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…
Bagged Regularized -Distances for Anomaly Detection
Yuchao Cai, Hanfang Yang, Yuheng Ma +1
We consider the paradigm of unsupervised anomaly detection, which involves the identification of anomalies within a dataset in the absence of labeled examples. Though distance-base…
Bagged -Distance for Mode-Based Clustering Using the Probability of Localized Level Sets
Hanyuan Hang
In this paper, we propose an ensemble learning algorithm named \textit{bagged -distance for mode-based clustering} (\textit{BDMBC}) by putting forward a new measurement called t…
Local Adaptivity of Gradient Boosting in Histogram Transform Ensemble Learning
Hanyuan Hang
In this paper, we propose a gradient boosting algorithm called \textit{adaptive boosting histogram transform} (\textit{ABHT}) for regression to illustrate the local adaptivity of g…
Under-bagging Nearest Neighbors for Imbalanced Classification
Hanyuan Hang, Yuchao Cai, Hanfang Yang +1
In this paper, we propose an ensemble learning algorithm called \textit{under-bagging -nearest neighbors} (\textit{under-bagging -NN}) for imbalanced classification problems.…
GBHT: Gradient Boosting Histogram Transform for Density Estimation
Jingyi Cui, Hanyuan Hang, Yisen Wang +1
In this paper, we propose a density estimation algorithm called \textit{Gradient Boosting Histogram Transform} (GBHT), where we adopt the \textit{Negative Log Likelihood} as the lo…
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…
Gradient Boosted Binary Histogram Ensemble for Large-scale Regression
Hanyuan Hang, Tao Huang, Yuchao Cai +2
In this paper, we propose a gradient boosting algorithm for large-scale regression problems called \textit{Gradient Boosted Binary Histogram Ensemble} (GBBHE) based on binary histo…
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…
Histogram Transform Ensembles for Density Estimation
Hanyuan Hang
We investigate an algorithm named histogram transform ensembles (HTE) density estimator whose effectiveness is supported by both solid theoretical analysis and significant experime…
Density-based Clustering with Best-scored Random Forest
Hanyuan Hang, Yuchao Cai, Hanfang Yang
Single-level density-based approach has long been widely acknowledged to be a conceptually and mathematically convincing clustering method. In this paper, we propose an algorithm c…
Best-scored Random Forest Classification
Hanyuan Hang, Xiaoyu Liu, Ingo Steinwart
We propose an algorithm named best-scored random forest for binary classification problems. The terminology "best-scored" means to select the one with the best empirical performanc…
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…
Two-stage Best-scored Random Forest for Large-scale Regression
Hanyuan Hang, Yingyi Chen, Johan A. K. Suykens
We propose a novel method designed for large-scale regression problems, namely the two-stage best-scored random forest (TBRF). "Best-scored" means to select one regression tree wit…
Optimal Learning with Anisotropic Gaussian SVMs
Hanyuan Hang, Ingo Steinwart
This paper investigates the nonparametric regression problem using SVMs with anisotropic Gaussian RBF kernels. Under the assumption that the target functions are resided in certain…
Matrix Infinitely Divisible Series: Tail Inequalities and Their Applications
Chao Zhang, Xianjie Gao, Min-Hsiu Hsieh +2
In this paper, we study tail inequalities of the largest eigenvalue of a matrix infinitely divisible (i.d.) series, which is a finite sum of fixed matrices weighted by i.d. random…
Kernel Density Estimation for Dynamical Systems
Hanyuan Hang, Ingo Steinwart, Yunlong Feng +1
We study the density estimation problem with observations generated by certain dynamical systems that admit a unique underlying invariant Lebesgue density. Observations drawn from…
Learning theory estimates with observations from general stationary stochastic processes
Hanyuan Hang, Yunlong Feng, Ingo Steinwart +1
This paper investigates the supervised learning problem with observations drawn from certain general stationary stochastic processes. Here by \emph{general}, we mean that many stat…