An information criterion for automatic gradient tree boosting

BooVAE: Boosting Approach for Continual Learning of VAE

Variational autoencoder (VAE) is a deep generative model for unsupervised learning, allowing to encode observations into the meaningful latent space. VAE is prone to catastrophic forgetting when tasks arrive sequentially, and only the data for the current one is available. We address this problem of continual learning for VAEs. It is known that the choice of the prior distribution over the latent space is crucial for VAE in the non-continual setting. We argue that it can also be helpful to avoid catastrophic forgetting. We learn the approximation of the aggregated posterior as a prior for each…

Trees-Based Models for Correlated Data

This paper presents a new approach for trees-based regression, such as simple regression tree, random forest and gradient boosting, in settings involving correlated data. We show the problems that arise when implementing standard trees-based regression models, which ignore the correlation structure. Our new approach explicitly takes the correlation structure into account in the splitting criterion, stopping rules and fitted values in the leaves, which induces some major modifications of standard methodology. The superiority of our new approach over trees-based models that do not account for th…

基于$\eta$-集成局部深度与自适应分组的自动深度局部中心聚类

Automatic depth-based local center clustering via $β$-integrated local depth and adaptive grouping

提出自动深度局部中心聚类（A-DLCC），利用β-集成局部深度识别局部中心，结合自适应合并准则，无需参数调整即可自动确定簇数并产生可解释聚类结果。

Stopping criterion for active learning based on deterministic generalization bounds

Active learning is a framework in which the learning machine can select the samples to be used for training. This technique is promising, particularly when the cost of data acquisition and labeling is high. In active learning, determining the timing at which learning should be stopped is a critical issue. In this study, we propose a criterion for automatically stopping active learning. The proposed stopping criterion is based on the difference in the expected generalization errors and hypothesis testing. We derive a novel upper bound for the difference in expected generalization errors before …

Prediction-based Variable Selection for Component-wise Gradient Boosting

Model-based component-wise gradient boosting is a popular tool for data-driven variable selection. In order to improve its prediction and selection qualities even further, several modifications of the original algorithm have been developed, that mainly focus on different stopping criteria, leaving the actual variable selection mechanism untouched. We investigate different prediction-based mechanisms for the variable selection step in model-based component-wise gradient boosting. These approaches include Akaikes Information Criterion (AIC) as well as a selection rule relying on the component-wi…

用于自动肘部检测的谱信息准则

Spectral information criterion for automatic elbow detection

本文提出无需似然函数、可提取误差曲线几何特征的谱信息准则SIC，用于自动检测肘部并筛选候选模型，实验验证了其有效性。