Optimal subsampling for softmax regression
WebApr 6, 2024 · The theory encompasses and generalises most existing methods in the field of optimal subdata selection based on unequal probability sampling and inverse probability weighting, and derives optimality conditions for a general class of optimality criteria. Subsampling is commonly used to overcome computational and economical bottlenecks … WebOptimal subsampling for softmax regression Article Full-text available Apr 2024 Yaqiong Yao Haiying Wang To meet the challenge of massive data, Wang et al. (J Am Stat Assoc 113 (522):829–844,...
Optimal subsampling for softmax regression
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WebNov 5, 2024 · Title: Optimal Poisson Subsampling for Softmax Regression Authors: Yaqiong Yao, Jiahui Zou Award ID(s): 2105571 Publication Date: 2024-11-05 NSF-PAR ID: … WebThis paper fills the gap by studying the subsampling method for a widely used missing data estimator, the augmented inverse probability weighting (AIPW) estimator. The response mean estimation problem with missing responses is discussed for illustration. A two-stage subsampling method is proposed via Poisson sampling framework.
WebLightGBM LightGBM（Light Gradient Boosting Machine）是一个基于梯度提升决策树（GBDT）的高效机器学习框架。它是由微软公司开发的，旨在提供更快、更高效的训练和预测性能。LightGBM在许多数据科学竞赛中都表现出色&am… WebMar 17, 2024 · This article focuses on quantile regression with massive data where the sample size n (greater than 1 0 6 in general) is extraordinarily large but the dimension d (smaller than 20 in general) is small. We first formulate the general subsampling procedure and establish the asymptotic property of the resultant estimator.
For the softmax regression model with massive data, we have established the asymptotic normality of the general subsampling estimator, and then derived optimal subsampling probabilities under the A-optimality criterion and the L-optimality with a specific L. See more As N\rightarrow \infty , {\mathbf {M}}_N=N^{-1}\sum _{i=1}^{N}{\varvec{\phi }}_i({\hat{\varvec{\beta }}}_{{\mathrm {full}}})\otimes ({\mathbf {x}}_i{\mathbf {x}}_i^\mathrm{T}) goes to a positive-definite matrix in … See more In this theorem, both n and N go to infinity, but there are no restrictions on their relative orders. Even if n is larger than N, the theorem is still … See more For k=2, 4, N^{-2}\sum _{i=1}^{N}\pi _i^{-1}\Vert {\mathbf {x}}_i\Vert ^k=O_P(1); and there exists some \delta >0 such that N^{-(2+\delta )}\sum … See more Under Assumptions 1 and 2, given the full data {\mathcal {D}}_N in probability, as n\rightarrow \infty and N\rightarrow \infty , the approximation error {\hat{\varvec{\beta … See more WebThis method was named as optimal subsampling methods motivated 4 fromtheA-optimalitycriterion(OSMAC),andwasimprovedinWang(2024b)byadopt-5 ing unweighted target functions for subsamples and Poisson subsampling. In addition 6 to logistic regression, OSMAC was investigated to include softmax regression (Yao and
WebMar 12, 2024 · The key idea of subsampling is to perform statistical analysis on a representative subsample drawn from the full data. It provides a practical solution to …
WebSubsampling techniques are efficient methods for handling big data. Quite a few optimal sampling methods have been developed for parametric models in which the loss … impact system llcWebSoftmax regression, a generalization of Logistic re-gression (LR) in the setting of multi-class classi-cation, has been widely used in many machine learning applications. However, the performance of softmax regression is extremely sensitive to the presence of noisy data and outliers. To address this issue, we propose a model of robust softmax ... impact t40 socketWebOptimal Subsampling for Softmax Regression 3 2 Model setup and optimal subsampling. Yaqiong Yao, Haiying Wang. Published 2024. Mathematics. To meet the challenge of … impact symptom checklistWebDec 1, 2024 · This paper focuses on a model-free subsampling method, called global likelihood subsampling, such that the subsample is robust to different model choices. It leverages the idea of the global... impact t1WebThis idea was generalized in [11] to softmax regression. An optimal subsampling method under the A-optimality criterion (OSMAC) for logistic re-gression inspired by the idea of optimal design of experiments was developed in [12]. They proposed to use a pilot subsample to estimate the optimal subsampling probabilities, which impact synonymousWebThe problem of variable selection in neural network regression models with dependent data is considered. In this framework, a test procedure based on the introduction of a measure for the variable re impact syscoWebOptimal Subsampling for Softmax Regression. Statistical Papers Languages Chinese Native or bilingual proficiency English Professional … impact t30