convex loss function

Kernel-based Distributed Learning JMLR 2026 Are Ensembles Getting Better All the Time? JMLR 2025 Discovering Preference Optimization Algorithms with and for Large Language Models NeurIPS 2024 Learning with Fitzpatrick Losses NeurIPS 2024 The Nyström method for convex loss functions JMLR 2024 Early Stopping for Iterative Regularization with General Loss Functions JMLR 2022 Shuffle Private Stochastic Convex Optimization ICLR 2022 Explaining fast improvement in online imitation learning UAI 2021 LQF: Linear Quadratic Fine-Tuning CVPR 2021 One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them JMLR 2021 Robust high dimensional learning for Lipschitz and convex losses JMLR 2020 Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms AISTATS 2019 Convex Elicitation of Continuous Properties NeurIPS 2018 Gradient Boosting on Stochastic Data Streams AISTATS 2017 Multi-Positive and Unlabeled Learning IJCAI 2017 Iterative Regularization for Learning with Convex Loss Functions JMLR 2016 Online Gradient Boosting NeurIPS 2015 GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare ICML 2014 Beating Bandits in Gradually Evolving Worlds COLT 2013 Online Optimization with Gradual Variations COLT 2012 Learning large-margin halfspaces with more malicious noise NeurIPS 2011 Composite Binary Losses JMLR 2010 Efficient algorithms for learning kernels from multiple similarity matrices with general convex loss functions NeurIPS 2010 On the Design of Loss Functions for Classification: theory, robustness to outliers, and SavageBoost NeurIPS 2008 Sparse Online Learning via Truncated Gradient NeurIPS 2008