Foolish Crowds Support Benign Overfitting

Foolish Crowds Support Benign Overfitting

6 October 2021

Niladri S. Chatterji

Papers citing "Foolish Crowds Support Benign Overfitting"

10 / 10 papers shown

Title
Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks Fanghui Liu L. Dadi V. Cevher 85 2 0 29 Apr 2024
Bayesian Analysis for Over-parameterized Linear Model via Effective Spectra Tomoya Wakayama Masaaki Imaizumi 60 1 0 25 May 2023
Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression Mo Zhou Rong Ge 49 2 0 01 Feb 2023
Strong inductive biases provably prevent harmless interpolation Michael Aerni Marco Milanta Konstantin Donhauser Fanny Yang 42 9 0 18 Jan 2023
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models Lijia Zhou Frederic Koehler Pragya Sur Danica J. Sutherland Nathan Srebro 88 9 0 21 Oct 2022
Deep Linear Networks can Benignly Overfit when Shallow Ones Do Niladri S. Chatterji Philip M. Long 28 8 0 19 Sep 2022
A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors Nikhil Ghosh M. Belkin 24 7 0 23 Jul 2022
Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting Neil Rohit Mallinar James B. Simon Amirhesam Abedsoltan Parthe Pandit M. Belkin Preetum Nakkiran 31 37 0 14 Jul 2022
Benign Overfitting in Adversarially Robust Linear Classification Jinghui Chen Yuan Cao Quanquan Gu AAML SILM 34 10 0 31 Dec 2021
Tight bounds for minimum l1-norm interpolation of noisy data Guillaume Wang Konstantin Donhauser Fanny Yang 81 20 0 10 Nov 2021