% Key papers for Fairness-constrained classification (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2017_on_56524,
  title = {{On Fairness and Calibration}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9342},
}

@inproceedings{icml2018_learning_36779,
  title = {{Learning Adversarially Fair and Transferable Representations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2393},
}

@inproceedings{icml2019_flexibly_37378,
  title = {{Flexibly Fair Representation Learning by Disentanglement}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4254},
}

@inproceedings{icml2018_a_36477,
  title = {{A Reductions Approach to Fair Classification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2361},
}

@inproceedings{icml2018_multicalibration_36860,
  title = {{Multicalibration: Calibration for the (Computationally-Identifiable) Masses}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2448},
}

@inproceedings{neurips2018_fairness_57123,
  title = {{Fairness Through Computationally-Bounded Awareness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11475},
}

@inproceedings{neurips2018_empirical_57093,
  title = {{Empirical Risk Minimization Under Fairness Constraints}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11286},
}

@inproceedings{aaai2020_pairwise_102108,
  title = {{Pairwise Fairness for Ranking and Regression}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5970},
}

@inproceedings{aaai2018_non_102395,
  title = {{Non-Discriminatory Machine Learning Through Convex Fairness Criteria}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11662},
}

@inproceedings{icml2020_minimax_38454,
  title = {{Minimax Pareto Fairness: A Multi Objective Perspective}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5925},
}

@inproceedings{aaai2022_on_2496,
  title = {{On the Fairness of Causal Algorithmic Recourse}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/09584-on-the-fairness-of-causal-algorithmic-recourse/},
}

@inproceedings{neurips2017_recycling_56616,
  title = {{Recycling Privileged Learning and Distribution Matching for Fairness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8863},
}

@inproceedings{iclr2020_training_29250,
  title = {{Training individually fair ML models with sensitive subspace robustness}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=B1gdkxHFDH},
}

@inproceedings{icml2019_fairness_37353,
  title = {{Fairness without Harm: Decoupled Classifiers with Preference Guarantees}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3684},
}

@inproceedings{icml2020_is_38348,
  title = {{Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6236},
}

@inproceedings{aaai2020_fairness_102140,
  title = {{Fairness for Robust Log Loss Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6002},
}

@inproceedings{aaai2019_convex_99957,
  title = {{Convex Formulations for Fair Principal Component Analysis}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/3843},
}

@inproceedings{neurips2020_robust_60738,
  title = {{Robust Optimization for Fairness with Noisy Protected Groups}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17129},
}

@inproceedings{icml2019_fairness_37354,
  title = {{Fairness-Aware Learning for Continuous Attributes and Treatments}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4130},
}

@inproceedings{iclr2021_fairbatch_30407,
  title = {{FairBatch: Batch Selection for Model Fairness}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2652},
}

@inproceedings{iclr2024_ffb_34535,
  title = {{FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18539},
}

@inproceedings{aaai2024_fair_5958,
  title = {{Fair and Optimal Prediction via Post-Processing}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30302},
}

@inproceedings{aaai2024_fairwasp_5962,
  title = {{FairWASP: Fast and Optimal Fair Wasserstein Pre-processing}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29545},
}

@inproceedings{iclr2024_post_34006,
  title = {{Post-hoc bias scoring is optimal for fair classification}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19069},
}

@inproceedings{aaai2026_fair_107156,
  title = {{Fair Domain Generalization: An Information-Theoretic View}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39508},
}

@inproceedings{aaai2025_searching_10702,
  title = {{Searching for Unfairness in Algorithms’ Outputs: Novel Tests and Insights}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34933},
}

@inproceedings{icml2024_fair_43524,
  title = {{Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34928},
}

@inproceedings{neurips2024_oxonfair_74666,
  title = {{OxonFair: A Flexible Toolkit for Algorithmic Fairness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/92930},
}

@inproceedings{neurips2025_efficient_75266,
  title = {{Efficient Fairness-Performance Pareto Front Computation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=65oFEAP42P},
}

@inproceedings{neurips2025_selective_75763,
  title = {{Selective Omniprediction and Fair Abstention}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=BoYGLpNXZd},
}

@inproceedings{iclr2025_rethinking_83504,
  title = {{Rethinking Fair Representation Learning for Performance-Sensitive Tasks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28312},
}

@inproceedings{aaai2026_bayes_107638,
  title = {{Bayes-Optimal Fair Classification with Multiple Sensitive Features}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39990},
}

@inproceedings{aaai2024_fair_5956,
  title = {{Fair Representation Learning with Maximum Mean Discrepancy Distance Constraint (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30476},
}

@inproceedings{aaai2025_froc_9115,
  title = {{FROC: Building Fair ROC from a Trained Classifier}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34836},
}

@inproceedings{aaai2025_mitigating_9958,
  title = {{Mitigating Bias in Machine Learning: A Comprehensive Review and Novel Approaches}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35107},
}

@inproceedings{iclr2024_on_35629,
  title = {{On the Fairness ROAD: Robust Optimization for Adversarial Debiasing}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17444},
}

@inproceedings{icml2024_on_43481,
  title = {{On the Maximal Local Disparity of Fairness-Aware Classifiers}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34971},
}

@inproceedings{icml2024_standardized_43801,
  title = {{Standardized Interpretable Fairness Measures for Continuous Risk Scores}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34651},
}

@inproceedings{icml2024_stability_44687,
  title = {{Stability and Multigroup Fairness in Ranking with Uncertain Predictions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33765},
}

@inproceedings{icml2025_accelerating_46898,
  title = {{Accelerating Spectral Clustering under Fairness Constraints}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45681},
}