A Theoretical Analysis of the Learning Dynamics under Class Imbalance

1 July 2022

Papers citing "A Theoretical Analysis of the Learning Dynamics under Class Imbalance"

8 / 8 papers shown

Title
When resampling/reweighting improves feature learning in imbalanced classification?: A toy-model study Tomoyuki Obuchi Toshiyuki Tanaka 44 0 0 09 Sep 2024
Restoring balance: principled under/oversampling of data for optimal classification Emanuele Loffredo Mauro Pastore Simona Cocco R. Monasson 35 9 0 15 May 2024
Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models Frederik Kunstner Robin Yadav Alan Milligan Mark Schmidt Alberto Bietti 29 26 0 29 Feb 2024
Can Continual Learning Improve Long-Tailed Recognition? Toward a Unified Framework Mahdiyar Molahasani Michael A. Greenspan Ali Etemad 24 2 0 23 Jun 2023
An Investigation of Why Overparameterization Exacerbates Spurious Correlations Shiori Sagawa Aditi Raghunathan Pang Wei Koh Percy Liang 144 370 0 09 May 2020
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 296 4,203 0 23 Aug 2019
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 119 1,198 0 16 Aug 2016
SMOTE: Synthetic Minority Over-sampling Technique Nitesh V. Chawla Kevin W. Bowyer Lawrence Hall W. Kegelmeyer AI4TS 160 25,244 0 09 Jun 2011