% Key papers for Consistency of surrogate losses (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2018_algorithms_56858,
  title = {{Algorithms and Theory for Multiple-Source Adaptation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11789},
}

@inproceedings{icml2016_asymmetric_35743,
  title = {{Asymmetric Multi-task Learning Based on Task Relatedness and Loss}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/leeb16.html},
}

@inproceedings{cvpr2019_a_13795,
  title = {{A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{neurips2016_a_55534,
  title = {{A Minimax Approach to Supervised Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7173},
}

@inproceedings{neurips2017_revenue_56627,
  title = {{Revenue Optimization with Approximate Bid Predictions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8975},
}

@inproceedings{neurips2016_optimal_55919,
  title = {{Optimal Black-Box Reductions Between Optimization Objectives}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7316},
}

@inproceedings{neurips2017_learning_56446,
  title = {{Learning with Average Top-k Loss}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8846},
}

@inproceedings{neurips2016_boosting_55607,
  title = {{Boosting with Abstention}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/6937},
}

@inproceedings{neurips2016_a_55530,
  title = {{A Consistent Regularization Approach for Structured Prediction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7356},
}

@inproceedings{neurips2019_robust_58919,
  title = {{Robust Bi-Tempered Logistic Loss Based on Bregman Divergences}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14408},
}

@inproceedings{aaai2017_generalized_104457,
  title = {{Generalized Ambiguity Decompositions for Classification with Applications in Active Learning and Unsupervised Ensemble Pruning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10834},
}

@inproceedings{icml2017_forest_36197,
  title = {{Forest-type Regression with General Losses and Robust Forest}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/871},
}

@inproceedings{neurips2021_on_61665,
  title = {{On UMAP's True Loss Function}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28679},
}

@inproceedings{neurips2016_optimizing_55927,
  title = {{Optimizing affinity-based binary hashing using auxiliary coordinates}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7177},
}

@inproceedings{aaai2020_online_102157,
  title = {{Online Active Learning of Reject Option Classifiers}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6019},
}

@inproceedings{icml2019_dimensionality_37291,
  title = {{Dimensionality Reduction for Tukey Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3896},
}

@inproceedings{neurips2016_adversarial_55569,
  title = {{Adversarial Multiclass Classification: A Risk Minimization Perspective}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7206},
}

@inproceedings{icml2021_on_39698,
  title = {{On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10279},
}

@inproceedings{cvpr2020_on_15979,
  title = {{On the Distribution of Minima in Intrinsic-Metric Rotation Averaging}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{neurips2019_deep_58111,
  title = {{Deep Supervised Summarization: Algorithm and Application to Learning Instructions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13335},
}

@inproceedings{neurips2024_multi_74622,
  title = {{Multi-Label Learning with Stronger Consistency Guarantees}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/92969},
}

@inproceedings{neurips2024_realizable_71951,
  title = {{Realizable \$H\$-Consistent and Bayes-Consistent Loss Functions for Learning to Defer}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95357},
}

@inproceedings{neurips2024_cardinality_72513,
  title = {{Cardinality-Aware Set Prediction and Top-\$k\$ Classification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94852},
}

@inproceedings{neurips2024_decision_73276,
  title = {{Decision-Focused Learning with Directional Gradients}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94165},
}

@inproceedings{icml2024_generalized_45043,
  title = {{Generalized Preference Optimization: A Unified Approach to Offline Alignment}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33409},
}

@inproceedings{iclr2024_a_34560,
  title = {{A representation-learning game for classes of prediction tasks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18514},
}

@inproceedings{iclr2024_learning_34803,
  title = {{Learning to Reject with a Fixed Predictor: Application to Decontextualization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18271},
}

@inproceedings{icml2024_criterion_44590,
  title = {{Criterion Collapse and Loss Distribution Control}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33862},
}

@inproceedings{icml2024_improving_45181,
  title = {{Improving SAM Requires Rethinking its Optimization Formulation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33271},
}

@inproceedings{icml2025_loss_47508,
  title = {{Loss Functions and Operators Generated by f-Divergences}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45088},
}

@inproceedings{icml2025_tilted_47892,
  title = {{Tilted Sharpness-Aware Minimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44711},
}

@inproceedings{icml2026_networked_54202,
  title = {{Networked Information Aggregation for Binary Classification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=mrtg4NmvAe},
}

@inproceedings{neurips2024_incorporating_72868,
  title = {{Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94535},
}

@inproceedings{neurips2024_trading_74477,
  title = {{Trading off Consistency and Dimensionality of Convex Surrogates for Multiclass Classification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93098},
}

@inproceedings{neurips2025_consistency_75208,
  title = {{Consistency Conditions for Differentiable Surrogate Losses}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=5UMRGlcatB},
}

@inproceedings{neurips2025_smart_75238,
  title = {{Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=5iAtDpkSZQ},
}

@inproceedings{neurips2025_how_77939,
  title = {{How to Learn a Star: Binary Classification with Starshaped Polyhedral Sets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=ZQ80jIrtzl},
}

@inproceedings{neurips2026_prediction_91791,
  title = {{Prediction Under Imperfect Compression: A Theory of Approximate MDL}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/150363},
}

@inproceedings{icml2024_finite_44418,
  title = {{Finite Smoothing Algorithm for High-Dimensional Support Vector Machines and Quantile Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34034},
}

@inproceedings{icml2025_spherical_46047,
  title = {{Spherical Rotation Dimension Reduction with Geometric Loss Functions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46713},
}