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Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers

28 April 2015

Papers citing "Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers"

45 / 45 papers shown

Title
Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests Brian Liu Rahul Mazumder 137 1 0 20 Feb 2024
The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness Yifan Hao Tong Zhang AAML 139 5 0 19 Jan 2024
Generalized equivalences between subsampling and ridge regularization Pratik V. Patil Jin-Hong Du 84 5 0 29 May 2023
Do Neural Networks Generalize from Self-Averaging Sub-classifiers in the Same Way As Adaptive Boosting? Michael Sun Peter Chatain AI4CE 71 0 0 14 Feb 2023
Bit Error and Block Error Rate Training for ML-Assisted Communication Reinhard Wiesmayr Gian Marti C. Dick Haochuan Song Christoph Studer 90 9 0 25 Oct 2022
Benefit of Interpolation in Nearest Neighbor Algorithms Yue Xing Qifan Song Guang Cheng 84 30 0 23 Feb 2022
Is interpolation benign for random forest regression? Ludovic Arnould Claire Boyer Erwan Scornet 66 6 0 08 Feb 2022
VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees Angelos Chatzimparmpas Rafael M. Martins Andreas Kerren 94 11 0 01 Dec 2021
VC dimension of partially quantized neural networks in the overparametrized regime Yutong Wang Clayton D. Scott 76 1 0 06 Oct 2021
Classification and Adversarial examples in an Overparameterized Linear Model: A Signal Processing Perspective Adhyyan Narang Vidya Muthukumar A. Sahai SILM AAML 64 1 0 27 Sep 2021
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning Yehuda Dar Vidya Muthukumar Richard G. Baraniuk 117 72 0 06 Sep 2021
Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation M. Belkin 65 186 0 29 May 2021
Latent Gaussian Model Boosting Fabio Sigrist AI4CE 32 23 0 19 May 2021
AdaBoost and robust one-bit compressed sensing Geoffrey Chinot Felix Kuchelmeister Matthias Löffler Sara van de Geer 82 5 0 05 May 2021
How rotational invariance of common kernels prevents generalization in high dimensions Konstantin Donhauser Mingqi Wu Fanny Yang 80 24 0 09 Apr 2021
Fitting Elephants P. Mitra 18 0 0 31 Mar 2021
Trees, Forests, Chickens, and Eggs: When and Why to Prune Trees in a Random Forest Siyu Zhou L. Mentch 55 22 0 30 Mar 2021
Deep learning: a statistical viewpoint Peter L. Bartlett Andrea Montanari Alexander Rakhlin 73 279 0 16 Mar 2021
Slow-Growing Trees Philippe Goulet Coulombe 57 1 0 02 Mar 2021
Global convergence of Negative Correlation Extreme Learning Machine Carlos Perales-González 119 2 0 30 Sep 2020
Distributional Generalization: A New Kind of Generalization Preetum Nakkiran Yamini Bansal OOD 78 42 0 17 Sep 2020
Data-assisted combustion simulations with dynamic submodel assignment using random forests Wai Tong Chung A. Mishra N. Perakis M. Ihme 125 43 0 08 Sep 2020
To Bag is to Prune Philippe Goulet Coulombe UQCV 40 9 0 17 Aug 2020
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation Vitaly Feldman Chiyuan Zhang TDI 245 472 0 09 Aug 2020
Multiple Descent: Design Your Own Generalization Curve Lin Chen Yifei Min M. Belkin Amin Karbasi DRL 157 61 0 03 Aug 2020
Boosting algorithms in energy research: A systematic review Hristos Tyralis Georgia Papacharalampous 93 45 0 01 Apr 2020
Margin Maximization as Lossless Maximal Compression N. Nikolaou Henry W. J. Reeve Gavin Brown 33 4 0 28 Jan 2020
Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy Michael Rapp E. Mencía Johannes Furnkranz FAtt 100 6 0 11 Nov 2019
Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success L. Mentch Siyu Zhou 67 71 0 01 Nov 2019
The Implicit Regularization of Ordinary Least Squares Ensembles Daniel LeJeune Hamid Javadi Richard G. Baraniuk 133 43 0 10 Oct 2019
Greedy Convex Ensemble Thanh Tan Nguyen N. Ye Peter L. Bartlett 31 1 0 09 Oct 2019
Keeping Designers in the Loop: Communicating Inherent Algorithmic Trade-offs Across Multiple Objectives Bowen Yu Ye Yuan Loren G. Terveen Zhiwei Steven Wu Jodi Forlizzi Haiyi Zhu 71 2 0 07 Oct 2019
Improving Outbreak Detection with Stacking of Statistical Surveillance Methods Moritz Kulessa E. Mencía Johannes Furnkranz 15 2 0 17 Jul 2019
Does Learning Require Memorization? A Short Tale about a Long Tail Vitaly Feldman TDI 165 503 0 12 Jun 2019
KTBoost: Combined Kernel and Tree Boosting Fabio Sigrist 69 27 0 11 Feb 2019
Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon Alexander Rakhlin Xiyu Zhai 127 79 0 28 Dec 2018
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 261 1,663 0 28 Dec 2018
Unknown Examples & Machine Learning Model Generalization Yeounoh Chung P. Haas E. Upfal Tim Kraska OOD 112 32 0 24 Aug 2018
Gradient and Newton Boosting for Classification and Regression Fabio Sigrist 69 62 0 09 Aug 2018
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate M. Belkin Daniel J. Hsu P. Mitra AI4CE 160 260 0 13 Jun 2018
Sharp Analysis of a Simple Model for Random Forests Jason M. Klusowski FAtt 52 22 0 07 May 2018
To understand deep learning we need to understand kernel learning M. Belkin Siyuan Ma Soumik Mandal 97 420 0 05 Feb 2018
Experimental Identification of Hard Data Sets for Classification and Feature Selection Methods with Insights on Method Selection Cuiju Luan Guozhu Dong 114 17 0 24 Mar 2017
An update on statistical boosting in biomedicine A. Mayr B. Hofner Elisabeth Waldmann Tobias Hepp O. Gefeller S. Meyer 40 32 0 27 Feb 2017
Sparse Projection Oblique Randomer Forests Tyler M. Tomita J. Browne Cencheng Shen Jaewon Chung Jesse Patsolic ... Jason Yim Carey E. Priebe Randal C. Burns Mauro Maggioni Joshua T. Vogelstein 108 44 0 10 Jun 2015