Lucas Mentch | DeepAI

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Wei Peng
70 publications
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Dongyeop Kang
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Duy Hoang Thai
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Daniel Fink
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Tim Coleman
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Leonard Stefanski
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Nicholas Kissel
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∙ 06/10/2021

Bias, Consistency, and Alternative Perspectives of the Infinitesimal Jackknife

Though introduced nearly 50 years ago, the infinitesimal jackknife (IJ) ...

0 Wei Peng, et al. ∙

∙ 03/30/2021

Trees, Forests, Chickens, and Eggs: When and Why to Prune Trees in a Random Forest

Due to their long-standing reputation as excellent off-the-shelf predict...

0 Siyu Zhou, et al. ∙

∙ 03/05/2021

Forward Stability and Model Path Selection

Most scientific publications follow the familiar recipe of (i) obtain da...

0 Nicholas Kissel, et al. ∙

∙ 02/23/2021

Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning

In 2001, Leo Breiman wrote of a divide between "data modeling" and "algo...

0 Lucas Mentch, et al. ∙

∙ 03/07/2020

Getting Better from Worse: Augmented Bagging and a Cautionary Tale of Variable Importance

As the size, complexity, and availability of data continues to grow, sci...

0 Lucas Mentch, et al. ∙

∙ 12/06/2019

On Racial Disparities in Recent Fatal Police Shootings

Fatal police shootings in the United States continue to be a polarizing ...

0 Lucas Mentch, et al. ∙

∙ 12/02/2019

Asymptotic Normality and Variance Estimation For Supervised Ensembles

Ensemble methods based on bootstrapping have improved the predictive acc...

0 Zhengze Zhou, et al. ∙

∙ 11/01/2019

Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success

Random forests remain among the most popular off-the-shelf supervised ma...

53 Lucas Mentch, et al. ∙

∙ 08/30/2019

Earlier Isn't Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization

Despite the recent developments on neural summarization systems, the und...

0 Taehee Jung, et al. ∙

∙ 08/27/2019

Locally Optimized Random Forests

Standard supervised learning procedures are validated against a test set...

24 Tim Coleman, et al. ∙

∙ 05/25/2019

Asymptotic Distributions and Rates of Convergence for Random Forests and other Resampled Ensemble Learners

Random forests remain among the most popular off-the-shelf supervised le...

0 Wei Peng, et al. ∙

∙ 05/01/2019

Please Stop Permuting Features: An Explanation and Alternatives

This paper advocates against permute-and-predict (PaP) methods for inter...

0 Giles Hooker, et al. ∙

∙ 04/16/2019

Scalable and Efficient Hypothesis Testing with Random Forests

Throughout the last decade, random forests have established themselves a...

0 Tim Coleman, et al. ∙

∙ 10/26/2017

Statistical Inference on Tree Swallow Migrations, Using Random Forests

Species migratory patterns have typically been studied through individua...

0 Tim Coleman, et al. ∙

∙ 06/29/2016

Multiphase Segmentation For Simultaneously Homogeneous and Textural Images

Segmentation remains an important problem in image processing. For homog...

0 Duy Hoang Thai, et al. ∙

∙ 06/01/2015

Bootstrap Bias Corrections for Ensemble Methods

This paper examines the use of a residual bootstrap for bias correction ...

0 Giles Hooker, et al. ∙

∙ 04/25/2014

Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests

This work develops formal statistical inference procedures for machine l...

0 Lucas Mentch, et al. ∙

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