margin theory

Regimes of No Gain in Multi-class Active Learning JMLR 2024 The Implicit Bias of Gradient Descent on Separable Multiclass Data NeurIPS 2024 Weisfeiler-Leman at the margin: When more expressivity matters ICML 2024 Feature Reconstruction From Outputs Can Mitigate Simplicity Bias in Neural Networks ICLR 2023 Scalable Optimal Margin Distribution Machine IJCAI 2023 The Implicit Bias of Benign Overfitting JMLR 2023 ExactBoost: Directly Boosting the Margin in Combinatorial and Non-decomposable Metrics AISTATS 2022 Multiclass learning with margin: exponential rates with no bias-variance trade-off ICML 2022 Label Distribution Learning Machine ICML 2021 Margins are Insufficient for Explaining Gradient Boosting NeurIPS 2020 Margin-Based Generalization Lower Bounds for Boosted Classifiers NeurIPS 2019 Learning Deep ResNet Blocks Sequentially using Boosting Theory ICML 2018 Nearly optimal classification for semimetrics JMLR 2017 Boosting with Abstention NeurIPS 2016 Nearly Optimal Classification for Semimetrics AISTATS 2016 Learning Halfspaces with the Zero-One Loss: Time-Accuracy Tradeoffs NeurIPS 2012 A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin JMLR 2011 Multiclass Boosting: Theory and Algorithms NeurIPS 2011 Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods JMLR 2004 The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins JMLR 2004