proper loss

Sample-Efficient Omniprediction for Proper Losses COLT 2026 Calm Composite Losses: Being Improper Yet Proper Composite AISTATS 2025 Proper losses regret at least 1/2-order JMLR 2025 Optimal Multiclass U-Calibration Error and Beyond NeurIPS 2024 LegendreTron: Uprising Proper Multiclass Loss Learning ICML 2023 Proper Losses for Discrete Generative Models ICML 2023 Proper Losses, Moduli of Convexity, and Surrogate Regret Bounds COLT 2023 The Geometry and Calculus of Losses JMLR 2023 When Does Optimizing a Proper Loss Yield Calibration? NeurIPS 2023 Being Properly Improper ICML 2022 Lower-Bounded Proper Losses for Weakly Supervised Classification ICML 2021 On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective CVPR 2021 Surrogate Regret Bounds for Polyhedral Losses NeurIPS 2021 All your loss are belong to Bayes NeurIPS 2020 Monge blunts Bayes: Hardness Results for Adversarial Training ICML 2019 A loss framework for calibrated anomaly detection NeurIPS 2018 Bipartite Ranking: a Risk-Theoretic Perspective JMLR 2016 Composite Multiclass Losses JMLR 2016 Bayes-Optimal Scorers for Bipartite Ranking COLT 2014 The Geometry of Losses COLT 2014 Surrogate Regret Bounds for the Area Under the ROC Curve via Strongly Proper Losses COLT 2013 A Characterization of Scoring Rules for Linear Properties COLT 2012 Mixability is Bayes Risk Curvature Relative to Log Loss JMLR 2012 Proper losses for learning from partial labels NeurIPS 2012 Composite Multiclass Losses NeurIPS 2011