More Data Can Hurt for Linear Regression: Sample-wise Double Descent

16 December 2019

Papers citing "More Data Can Hurt for Linear Regression: Sample-wise Double Descent"

18 / 18 papers shown

Title
The Double Descent Behavior in Two Layer Neural Network for Binary Classification Chathurika S Abeykoon A. Beknazaryan Hailin Sang 53 1 0 27 Apr 2025
How more data can hurt: Instability and regularization in next-generation reservoir computing Yuanzhao Zhang Edmilson Roque dos Santos Sean P. Cornelius 85 2 0 28 Jan 2025
Gibbs-Based Information Criteria and the Over-Parameterized Regime Haobo Chen Yuheng Bu Greg Wornell 27 1 0 08 Jun 2023
Collaborative Development of NLP models Fereshte Khani Marco Tulio Ribeiro 32 2 0 20 May 2023
High Dimensional Binary Classification under Label Shift: Phase Transition and Regularization Jiahui Cheng Minshuo Chen Hao Liu Tuo Zhao Wenjing Liao 36 0 0 01 Dec 2022
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance Marco Loog T. Viering 26 1 0 25 Nov 2022
Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models Ethan Pickering T. Sapsis 24 6 0 27 Aug 2022
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes Shivam Garg Dimitris Tsipras Percy Liang Gregory Valiant 26 449 0 01 Aug 2022
Regularization-wise double descent: Why it occurs and how to eliminate it Fatih Yilmaz Reinhard Heckel 30 11 0 03 Jun 2022
Contrasting random and learned features in deep Bayesian linear regression Jacob A. Zavatone-Veth William L. Tong Cengiz Pehlevan BDL MLT 28 26 0 01 Mar 2022
Differentially Private Regression with Unbounded Covariates Jason Milionis Alkis Kalavasis Dimitris Fotakis Stratis Ioannidis 23 10 0 19 Feb 2022
Classification and Adversarial examples in an Overparameterized Linear Model: A Signal Processing Perspective Adhyyan Narang Vidya Muthukumar A. Sahai SILM AAML 36 1 0 27 Sep 2021
The Shape of Learning Curves: a Review T. Viering Marco Loog 18 122 0 19 Mar 2021
Memorizing without overfitting: Bias, variance, and interpolation in over-parameterized models J. Rocks Pankaj Mehta 18 41 0 26 Oct 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 41 149 0 16 May 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
A Model of Double Descent for High-dimensional Binary Linear Classification Zeyu Deng A. Kammoun Christos Thrampoulidis 36 144 0 13 Nov 2019
Optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization D. Kobak Jonathan Lomond Benoit Sanchez 30 89 0 28 May 2018