Unraveling the Enigma of Double Descent: An In-depth Analysis through the Lens of Learned Feature Space

20 October 2023

Papers citing "Unraveling the Enigma of Double Descent: An In-depth Analysis through the Lens of Learned Feature Space"

8 / 8 papers shown

Title
Robust Training under Label Noise by Over-parameterization Sheng Liu Zhihui Zhu Qing Qu Chong You NoLa OOD 63 108 0 28 Feb 2022
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning Yehuda Dar Vidya Muthukumar Richard G. Baraniuk 71 72 0 06 Sep 2021
Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks Xiangyu Chang Yingcong Li Samet Oymak Christos Thrampoulidis 56 50 0 16 Dec 2020
Classification vs regression in overparameterized regimes: Does the loss function matter? Vidya Muthukumar Adhyyan Narang Vignesh Subramanian M. Belkin Daniel J. Hsu A. Sahai 78 151 0 16 May 2020
A Brief Prehistory of Double Descent Marco Loog T. Viering A. Mey Jesse H. Krijthe David Tax 43 69 0 07 Apr 2020
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks Zitong Yang Yaodong Yu Chong You Jacob Steinhardt Yi-An Ma 65 184 0 26 Feb 2020
Surprises in High-Dimensional Ridgeless Least Squares Interpolation Trevor Hastie Andrea Montanari Saharon Rosset Robert Tibshirani 167 743 0 19 Mar 2019
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 215 1,638 0 28 Dec 2018