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Found 20 papers, 1 papers with code
Boosted Histogram Transform for Regression
In this paper, we propose a boosting algorithm for regression problems called \textit{boosted histogram transform for regression} (BHTR) based on histogram transforms composed of random rotations, stretchings, and translations.
Class Probability Matching Using Kernel Methods for Label Shift Adaptation
In covariate shift adaptation where the differences in data distribution arise from variations in feature probabilities, existing approaches naturally address this problem based on \textit{feature probability matching} (\textit{FPM}).
Bagged Regularized $k$-Distances for Anomaly Detection
We consider the paradigm of unsupervised anomaly detection, which involves the identification of anomalies within a dataset in the absence of labeled examples.
Bagged $k$-Distance for Mode-Based Clustering Using the Probability of Localized Level Sets
On the theoretical side, we show that with a properly chosen number of nearest neighbors $k_D$ in the bagged $k$-distance, the sub-sample size $s$, the bagging rounds $B$, and the number of nearest neighbors $k_L$ for the localized level sets, BDMBC can achieve optimal convergence rates for mode estimation.
Local Adaptivity of Gradient Boosting in Histogram Transform Ensemble Learning
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 gradient boosting algorithms in histogram transform ensemble learning.
Under-bagging Nearest Neighbors for Imbalanced Classification
In this paper, we propose an ensemble learning algorithm called \textit{under-bagging $k$-nearest neighbors} (\textit{under-bagging $k$-NN}) for imbalanced classification problems.
GBHT: Gradient Boosting Histogram Transform for Density Estimation
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 loss function to make the boosting procedure available for the unsupervised tasks.
Leveraged Weighted Loss for Partial Label Learning
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 them is true.
Gradient Boosted Binary Histogram Ensemble for Large-scale Regression
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 histogram partition and ensemble learning.
OT-LLP: Optimal Transport for Learning from Label Proportions
Learning from label proportions (LLP), where the training data are arranged in form of groups with only label proportions provided instead of the exact labels, is an important weakly supervised learning paradigm in machine learning.
MMD GAN with Random-Forest Kernels
In this paper, we propose a novel kind of kernel, random forest kernel, to enhance the empirical performance of MMD GAN.
Histogram Transform Ensembles for Large-scale Regression
Instead, we apply kernel histogram transforms (KHT) equipped with smoother regressors such as support vector machines (SVMs), and it turns out that both single and ensemble KHT enjoy almost optimal convergence rates.
Histogram Transform Ensembles for Density Estimation
We investigate an algorithm named histogram transform ensembles (HTE) density estimator whose effectiveness is supported by both solid theoretical analysis and significant experimental performance.
Density-based Clustering with Best-scored Random Forest
Single-level density-based approach has long been widely acknowledged to be a conceptually and mathematically convincing clustering method.
Best-scored Random Forest Classification
We propose an algorithm named best-scored random forest for binary classification problems.
Best-scored Random Forest Density Estimation
Thirdly, the convergence rates under $L_{\infty}$-norm is presented.
Two-stage Best-scored Random Forest for Large-scale Regression
We propose a novel method designed for large-scale regression problems, namely the two-stage best-scored random forest (TBRF).
Optimal Learning with Anisotropic Gaussian SVMs
This paper investigates the nonparametric regression problem using SVMs with anisotropic Gaussian RBF kernels.
Kernel Density Estimation for Dynamical Systems
We study the density estimation problem with observations generated by certain dynamical systems that admit a unique underlying invariant Lebesgue density.
Learning theory estimates with observations from general stationary stochastic processes
We show that when the stochastic processes satisfy a generalized Bernstein-type inequality, a unified treatment on analyzing the learning schemes with various mixing processes can be conducted and a sharp oracle inequality for generic regularized empirical risk minimization schemes can be established.
