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The Infinitesimal Jackknife is a general method for estimating variances of parametric models, and more recently also for some ensemble methods.
Bias and confidence in not quite large samples
Tukey, J. (1958) · 1958
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On estimating regression
Nadaraya, E. A. (1964) · 1964
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Smooth regression analysis
Watson, G. S. (1964) · 1964
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The infinitesimal jackknife
Jaeckel, L. A. (1972) · 1972
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The jackknife, the bootstrap and other resampling plans
Efron, B. (1982) · 1982
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Stochastic gradient learning in neural networks
Bottou, L. et al. (1991) · 1991
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Extremum estimators
Hayashi, F. (2000) · 2000
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Greedy function approximation: a gradient boosting machine
Friedman, J. H. (2001) · 2001
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Variance components
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Scikit-learn: Machine learning in Python
Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay (2011) · 2011
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Efficient nested simulation for estimating the variance of a conditional expectation
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Nonparametric smoothing and lack-of-fit tests
Hart, J. (2013) · 2013
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Efron, B. (2014) · 2014
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Adam: A method for stochastic optimization
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Confidence intervals for random forests: the jackknife and the infinitesimal jackknife
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Tensorflow: A system for large-scale machine learning
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Boosting random forests to reduce bias; one-step boosted forest and its variance estimate
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Optimization for deep learning: An overview
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Generalised boosted forests
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Quantifying epistemic uncertainty in deep learning
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A unified framework for random forest prediction error estimation
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V-statistics and variance estimation
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