Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition

4 November 2020

Papers citing "Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition"

18 / 18 papers shown

Title
auto-fpt: Automating Free Probability Theory Calculations for Machine Learning Theory Arjun Subramonian Elvis Dohmatob 24 0 0 14 Apr 2025
Analysis of Overparameterization in Continual Learning under a Linear Model Daniel Goldfarb Paul Hand CLL 39 0 0 11 Feb 2025
Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer Blake Bordelon C. Pehlevan AI4CE 61 1 0 04 Feb 2025
Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis Yufan Li Subhabrata Sen Ben Adlam MLT 45 1 0 18 Apr 2024
A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks Behrad Moniri Donghwan Lee Hamed Hassani Edgar Dobriban MLT 34 19 0 11 Oct 2023
Gibbs-Based Information Criteria and the Over-Parameterized Regime Haobo Chen Yuheng Bu Greg Wornell 21 1 0 08 Jun 2023
Generalized equivalences between subsampling and ridge regularization Pratik V. Patil Jin-Hong Du 24 5 0 29 May 2023
Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation Jin-Hong Du Pratik V. Patil Arun K. Kuchibhotla 16 11 0 25 Apr 2023
Pathologies of Predictive Diversity in Deep Ensembles Taiga Abe E. Kelly Buchanan Geoff Pleiss John P. Cunningham UQCV 38 13 0 01 Feb 2023
Demystifying Disagreement-on-the-Line in High Dimensions Dong-Hwan Lee Behrad Moniri Xinmeng Huang Edgar Dobriban Hamed Hassani 21 8 0 31 Jan 2023
Gradient flow in the gaussian covariate model: exact solution of learning curves and multiple descent structures Antione Bodin N. Macris 34 4 0 13 Dec 2022
Regularization-wise double descent: Why it occurs and how to eliminate it Fatih Yilmaz Reinhard Heckel 25 11 0 03 Jun 2022
Generalization Through The Lens Of Leave-One-Out Error Gregor Bachmann Thomas Hofmann Aurélien Lucchi 46 7 0 07 Mar 2022
Contrasting random and learned features in deep Bayesian linear regression Jacob A. Zavatone-Veth William L. Tong C. Pehlevan BDL MLT 28 26 0 01 Mar 2022
Deep Ensembles Work, But Are They Necessary? Taiga Abe E. Kelly Buchanan Geoff Pleiss R. Zemel John P. Cunningham OOD UQCV 36 59 0 14 Feb 2022
Understanding the bias-variance tradeoff of Bregman divergences Ben Adlam Neha Gupta Zelda E. Mariet Jamie Smith UQCV UD 17 6 0 08 Feb 2022
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond Fanghui Liu Xiaolin Huang Yudong Chen Johan A. K. Suykens BDL 34 172 0 23 Apr 2020
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime Stéphane dÁscoli Maria Refinetti Giulio Biroli Florent Krzakala 93 152 0 02 Mar 2020