% Key papers for Minimax fairness via sampling (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2018_fairness_36683,
  title = {{Fairness Without Demographics in Repeated Loss Minimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2434},
}

@inproceedings{neurips2019_policy_58810,
  title = {{Policy Learning for Fairness in Ranking}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13674},
}

@inproceedings{icml2020_data_38075,
  title = {{Data preprocessing to mitigate bias: A maximum entropy based approach}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6215},
}

@inproceedings{icml2019_the_37793,
  title = {{The Implicit Fairness Criterion of Unconstrained Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4223},
}

@inproceedings{icml2019_repairing_37696,
  title = {{Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3898},
}

@inproceedings{neurips2018_on_57465,
  title = {{On preserving non-discrimination when combining expert advice}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11801},
}

@inproceedings{icml2022_active_40209,
  title = {{Active Sampling for Min-Max Fairness}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/18331},
}

@inproceedings{neurips2020_trade_61003,
  title = {{Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17285},
}

@inproceedings{icml2023_fair_41573,
  title = {{Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/25191},
}

@inproceedings{neurips2021_unintended_61957,
  title = {{Unintended Selection: Persistent Qualification Rate Disparities and Interventions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/31630},
}

@inproceedings{aaai2024_providing_7024,
  title = {{Providing Fair Recourse over Plausible Groups}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30175},
}

@inproceedings{icml2023_when_41648,
  title = {{When Personalization Harms Performance: Reconsidering the Use of Group Attributes in Prediction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24280},
}

@inproceedings{icml2021_multi_39636,
  title = {{Multi-group Agnostic PAC Learnability}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10535},
}

@inproceedings{icml2023_omnipredictors_42642,
  title = {{Omnipredictors for Constrained Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24471},
}

@inproceedings{aaai2025_active_8010,
  title = {{Active Fourier Auditor for Estimating Distributional Properties of ML Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33682},
}

@inproceedings{neurips2024_fairness_72565,
  title = {{Fairness-Aware Estimation of Graphical Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94807},
}

@inproceedings{neurips2024_fairness_72694,
  title = {{Fairness without Harm: An Influence-Guided Active Sampling Approach}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94692},
}

@inproceedings{iclr2024_f_35410,
  title = {{f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17663},
}

@inproceedings{icml2022_selective_41158,
  title = {{Selective Regression under Fairness Criteria}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/18233},
}

@inproceedings{icml2022_simple_41176,
  title = {{Simple and near-optimal algorithms for hidden stratification and multi-group learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16079},
}

@inproceedings{icml2025_meta_48282,
  title = {{Meta Optimality for Demographic Parity Constrained Regression via Post-Processing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44330},
}

@inproceedings{icml2026_maximin_50137,
  title = {{Maximin Relative Improvement: Fair Learning as a Bargaining Problem}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=9KOJdlFH82},
}

@inproceedings{icml2026_correcting_50457,
  title = {{Correcting Overparameterization Effects in Fair Empirical Risk Minimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=CXAiXFaT3f},
}

@inproceedings{neurips2025_matchings_78177,
  title = {{Matchings Under Biased and Correlated Evaluations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=bsjJ7c1mEV},
}

@inproceedings{iclr2025_no_83044,
  title = {{No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28761},
}

@inproceedings{neurips2026_demographic_90972,
  title = {{Demographic parity in regression and classification within the unawareness framework}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/156660},
}

@inproceedings{neurips2026_geometry_93173,
  title = {{Geometry of Relaxed Fair Regression: A Unified Framework for Aware and Unaware Settings}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/153522},
}

@inproceedings{icml2025_doubly_45938,
  title = {{Doubly Robust Fusion of Many Treatments for Policy Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46608},
}

@inproceedings{icml2025_bridging_47116,
  title = {{Bridging Fairness and Efficiency in Conformal Inference: A Surrogate-Assisted Group-Clustered Approach}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45471},
}

@inproceedings{icml2026_a_51916,
  title = {{A General Framework for Fair and Robust Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=R4W0oOjCKG},
}

@inproceedings{iclr2026_t_85883,
  title = {{T-TAMER: Provably Taming Trade-offs in ML Serving}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=JFY9MZtWTu},
}