exponential loss

Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks ICML 2025 Discovering Preference Optimization Algorithms with and for Large Language Models NeurIPS 2024 Lexicographic Optimization: Algorithms and Stability AISTATS 2024 Boosting with Tempered Exponential Measures NeurIPS 2023 The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks ICLR 2023 Fast margin maximization via dual acceleration ICML 2021 On the Characterization of a Class of Fisher-Consistent Loss Functions and its Application to Boosting JMLR 2016 Rademacher Observations, Private Data, and Boosting ICML 2015 The Rate of Convergence of AdaBoost JMLR 2013 The Fast Convergence of Boosting NeurIPS 2011 The Rate of Convergence of Adaboost COLT 2011 Variance Penalizing AdaBoost NeurIPS 2011 A PAC-Bayes Risk Bound for General Loss Functions NeurIPS 2006