Statistical Applications in Genetics and Molecular Biology Super Learner

By M. J. van der Laan, E. Polley, Alan E. Hubbard. 2011

1,294 citations recorded by the source corpus.

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Citations in the corpus

Quasi-oracle estimation of heterogeneous treatment effects (2017, methodology): “Another option would have been to try to combine predictions from the lasso and boosting models, as advocated by Van der Laan, Polley, and Hubbard (2007).”
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data (2020, methodology): “The stacker model can improve upon shortcomings of the individual base predictions and exploit interactions between base models that offer enhanced predictive power (Van der Laan et al., 2007).”
Nonparametric efficient causal mediation with intermediate confounders (2019, methodology): “Since it is generally not possible to know a priori which regression algorithm will be most appropriate for a given problem, we use an ensemble learning method known as the super learner (van der Laan et al., 2007).”
The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification (2017, methodology): “This is known as the discrete Super Learner selector [Van Der Laan and Dudoit, 2003, van der Laan et al., 2007, Polley and Van Der Laan, 2010], which asymptotically performs as well as the best base learner in the library, even as the number of candidates grows polynomial in sample size.”
Stacked Generalization: An Introduction to Super Learning (2017, positive): “Combining the ^ Y gam (cid:5) cv and ^ Y earth (cid:5) cv under these constraints (non-negative estimates that sum to 1) is referred to as a ‘‘convex combination,’’ and is motivated by both theoretical results and improved stability in practice [2, 5].”
Handbook of Big Data (2016, neutral): “Otherwise the associations look simply monotone and seem well summarized by correlations.”