surrogate loss

Inference with non-differentiable surrogate loss in a general high-dimensional classification framework JMLR 2026 A Two-Stage Learning-to-Defer Approach for Multi-Task Learning ICML 2025 Contextual Optimization Under Model Misspecification: A Tractable and Generalizable Approach ICML 2025 Enhanced H-Consistency Bounds ALT 2025 EnsLoss: Stochastic Calibrated Loss Ensembles for Preventing Overfitting in Classification ICML 2025 Learning Cascade Ranking as One Network ICML 2025 Locally Convex Global Loss Network for Decision-Focused Learning AAAI 2025 Mastering Multiple-Expert Routing: Realizable H-Consistency and Strong Guarantees for Learning to Defer ICML 2025 MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework CVPR 2025 Solving hidden monotone variational inequalities with surrogate losses ICLR 2025 H-Consistency Guarantees for Regression ICML 2024 A Universal Growth Rate for Learning with Smooth Surrogate Losses NeurIPS 2024 Cardinality-Aware Set Prediction and Top-k Classification NeurIPS 2024 Consistent Hierarchical Classification with A Generalized Metric AISTATS 2024 Decision-Focused Learning with Directional Gradients NeurIPS 2024 Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification JMLR 2024 FAKD: Feature Augmented Knowledge Distillation for Semantic Segmentation WACV 2024 FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction NeurIPS 2024 Fusing Individualized Treatment Rules Using Secondary Outcomes AISTATS 2024 Learning to Reject with a Fixed Predictor: Application to Decontextualization ICLR 2024 Multi-Label Learning with Stronger Consistency Guarantees NeurIPS 2024 Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms ALT 2024 Realizable H-Consistent and Bayes-Consistent Loss Functions for Learning to Defer NeurIPS 2024 Regression with Multi-Expert Deferral ICML 2024 Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention AISTATS 2024