% Key papers for Ranking-based loss optimization (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2023_cross_42162,
  title = {{Cross-Entropy Loss Functions: Theoretical Analysis and Applications}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24086},
}

@inproceedings{cvpr2021_an_16595,
  title = {{An Alternative Probabilistic Interpretation of the Huber Loss}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{iclr2018_smooth_28525,
  title = {{Smooth Loss Functions for Deep Top-k Classification}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/170},
}

@inproceedings{aaai2017_multiclass_104571,
  title = {{Multiclass Capped ℓp-Norm SVM for Robust Classifications}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10948},
}

@inproceedings{aaai2016_discriminative_105041,
  title = {{Discriminative Analysis Dictionary Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10213},
}

@inproceedings{cvpr2016_loss_11687,
  title = {{Loss Functions for Top-k Error: Analysis and Insights}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{cvpr2018_efficient_13056,
  title = {{Efficient Optimization for Rank-Based Loss Functions}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Mohapatra_Efficient_Optimization_for_CVPR_2018_paper.html},
}

@inproceedings{aaai2019_improving_99673,
  title = {{Improving One-Class Collaborative Filtering via Ranking-Based Implicit Regularizer}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/3766},
}

@inproceedings{iclr2020_neurquri_29914,
  title = {{NeurQuRI: Neural Question Requirement Inspector for Answerability Prediction in Machine Reading Comprehension}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=ryxgsCVYPr},
}

@inproceedings{aaai2022_rethinking_2646,
  title = {{Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones Is Enough}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/01518-rethinking-the-optimization-of-average-precision-only-penalizing-negative-instances-before-positive-ones-is-enough/},
}

@inproceedings{icml2017_consistency_36108,
  title = {{Consistency Analysis for Binary Classification Revisited}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/787},
}

@inproceedings{icml2018_binary_36549,
  title = {{Binary Classification with Karmic, Threshold-Quasi-Concave Metrics}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2232},
}

@inproceedings{neurips2021_label_62341,
  title = {{Label-Imbalanced and Group-Sensitive Classification under Overparameterization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26347},
}

@inproceedings{neurips2021_robust_62379,
  title = {{Robust and Decomposable Average Precision for Image Retrieval}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/25918},
}

@inproceedings{neurips2021_pirank_62738,
  title = {{PiRank: Scalable Learning To Rank via Differentiable Sorting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28506},
}

@inproceedings{neurips2022_are_64129,
  title = {{Are All Losses Created Equal: A Neural Collapse Perspective}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53974},
}

@inproceedings{icml2019_metric_37540,
  title = {{Metric-Optimized Example Weights}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3932},
}

@inproceedings{neurips2024_wormhole_73300,
  title = {{Wormhole Loss for Partial Shape Matching}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94143},
}

@inproceedings{icml2020_supervised_38824,
  title = {{Supervised learning: no loss no cry}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6079},
}

@inproceedings{neurips2020_all_59361,
  title = {{All your loss are belong to Bayes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18641},
}

@inproceedings{neurips2024_psl_72026,
  title = {{PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95290},
}

@inproceedings{aaai2026_marginalized_101204,
  title = {{Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Convex Parametric Shapes}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/37505},
}

@inproceedings{neurips2024_learning_70650,
  title = {{Learning with Fitzpatrick Losses}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96472},
}

@inproceedings{icml2024_fafe_44179,
  title = {{FAFE: Immune Complex Modeling with Geodesic Distance Loss on Noisy Group Frames}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34273},
}

@inproceedings{icml2025_a_47131,
  title = {{A Versatile Influence Function for Data Attribution with Non-Decomposable Loss}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45456},
}

@inproceedings{icml2025_principled_47704,
  title = {{Principled Algorithms for Optimizing Generalized Metrics in Binary Classification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44895},
}

@inproceedings{iclr2025_binary_80798,
  title = {{Binary Losses for Density Ratio Estimation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30970},
}

@inproceedings{icml2024_learning_43807,
  title = {{Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34645},
}

@inproceedings{iclr2025_proximal_80970,
  title = {{Proximal Mapping Loss: Understanding Loss Functions in Crowd Counting \& Localization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30798},
}

@inproceedings{neurips2026_serialization_93947,
  title = {{Serialization Tax in Shared-Latent Exchangeable Decisions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/148577},
}

@inproceedings{neurips2026_post_94276,
  title = {{Post-Processing Guarantees for Classification under Linear-Fractional Performance Metrics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/155732},
}