Recent advances in scaling-down sampling methods in machine learning

DOI10.1002/WICS.1414zbMATH Open1545.62041MaRDI QIDQ6607064

Janusz Wojtusiak, Amr ElRafey

Publication date: 17 September 2024

Published in: Wiley Interdisciplinary Reviews. WIREs Computational Statistics (Search for Journal in Brave)

zbMATH Keywords

data miningevolutionary computationmachine learninghealth informatics

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Cites Work

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• Bagging predictors
• Quantile regression with clustered data
• Is combining classifiers with stacking better than selecting the best one?
• Selective sampling using the query by committee algorithm
• A decision-theoretic generalization of on-line learning and an application to boosting
• Bayesian experimental design: A review
• Bootstrapping for highly unbalanced clustered data
• Evidence-based uncertainty sampling for active learning
• Stratified sampling for data mining on the deep web
• Generalized bootstrap for estimating equations
• The true sample complexity of active learning
• Geometry of \(E\)-optimality
• Active learning for logistic regression: an evaluation
• Rates of convergence in active learning
• Two faces of active learning
• Agnostic active learning
• Adaptive Cluster Sampling
• Minimax analysis of active learning
• Active Learning
• A Direct Bootstrap Method for Complex Sampling Designs From a Finite Population
• Divergence measures based on the Shannon entropy
• Bootstrapping data with multiple levels of variation
• Random sampling with a reservoir
• Statistics and Causal Inference
• 10.1162/153244302760200678
• Theory of Disagreement-Based Active Learning
• Bootstrapping Clustered Data
• Adjusting Treatment Effect Estimates by Post-Stratification in Randomized Experiments
• Activized Learning: Transforming Passive to Active with Improved Label Complexity
• Maximum Likelihood Inference On A Mixed Conditionally and Marginally Specified Regression Model for Genetic Epidemiologic Studies with Two-Phase Sampling
• Weighted Likelihood for Semiparametric Models and Two‐phase Stratified Samples, with Application to Cox Regression
• Margin Based Active Learning
• Teaching Dimension and the Complexity of Active Learning
• Designs for Regression Problems with Correlated Errors
• Principal Components Analysis Based on Multivariate MM Estimators With Fast and Robust Bootstrap
• Contribution to the Theory of Sampling Human Populations
• On Information and Sufficiency
• Optimum Allocation in Linear Regression Theory
• On the Efficient Design of Statistical Investigations
• Some Useful Moment Results in Sampling Problems
• Estimating Desired Sample Size for Simple Random Sampling of a Skewed Population

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