% Key papers for Post-processing and transport for fairness (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2018_preventing_36930,
  title = {{Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2241},
}

@inproceedings{neurips2018_why_57782,
  title = {{Why Is My Classifier Discriminatory?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11355},
}

@inproceedings{neurips2017_when_56775,
  title = {{When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9411},
}

@inproceedings{aaai2019_one_101212,
  title = {{One-Network Adversarial Fairness}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4085},
}

@inproceedings{icml2018_residual_36960,
  title = {{Residual Unfairness in Fair Machine Learning from Prejudiced Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2314},
}

@inproceedings{neurips2018_does_57066,
  title = {{Does mitigating ML's impact disparity require treatment disparity?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11778},
}

@inproceedings{icml2019_fair_37350,
  title = {{Fair Regression: Quantitative Definitions and Reduction-Based Algorithms}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4075},
}

@inproceedings{icml2018_nonconvex_36877,
  title = {{Nonconvex Optimization for Regression with Fairness Constraints}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2037},
}

@inproceedings{icml2018_fair_36682,
  title = {{Fair and Diverse DPP-Based Data Summarization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2176},
}

@inproceedings{neurips2019_unlocking_59150,
  title = {{Unlocking Fairness: a Trade-off Revisited}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13931},
}

@inproceedings{neurips2019_pc_58782,
  title = {{PC-Fairness: A Unified Framework for Measuring Causality-based Fairness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13516},
}

@inproceedings{icml2019_fairness_37352,
  title = {{Fairness risk measures}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3728},
}

@inproceedings{aaai2021_robust_1288,
  title = {{Robust Fairness Under Covariate Shift}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/09419-robust-fairness-under-covariate-shift/},
}

@inproceedings{neurips2019_the_59060,
  title = {{The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13519},
}

@inproceedings{aaai2021_controllable_325,
  title = {{Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/07610-controllable-guarantees-for-fair-outcomes-via-contrastive-information-estimation/},
}

@inproceedings{icml2018_blind_36554,
  title = {{Blind Justice: Fairness with Encrypted Sensitive Attributes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1906},
}

@inproceedings{neurips2019_noise_58680,
  title = {{Noise-tolerant fair classification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13270},
}

@inproceedings{neurips2019_inherent_58415,
  title = {{Inherent Tradeoffs in Learning Fair Representations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14455},
}

@inproceedings{icml2020_fair_38193,
  title = {{Fair Learning with Private Demographic Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6499},
}

@inproceedings{icml2020_two_38891,
  title = {{Two Simple Ways to Learn Individual Fairness Metrics from Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6761},
}

@inproceedings{aaai2024_arbitrariness_5204,
  title = {{Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30203},
}

@inproceedings{aaai2024_a_5065,
  title = {{A Sequentially Fair Mechanism for Multiple Sensitive Attributes}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29143},
}

@inproceedings{aaai2024_fairness_5964,
  title = {{Fairness with Censorship: Bridging the Gap between Fairness Research and Real-World Deployment}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30301},
}

@inproceedings{iclr2024_fairseg_35338,
  title = {{FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17735},
}

@inproceedings{iclr2024_unprocessing_35068,
  title = {{Unprocessing Seven Years of Algorithmic Fairness}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18005},
}

@inproceedings{icml2024_fair_44106,
  title = {{Fair Resource Allocation in Multi-Task Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34346},
}

@inproceedings{neurips2024_the_72245,
  title = {{The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97682},
}

@inproceedings{aaai2024_fairness_5963,
  title = {{Fairness under Covariate Shift: Improving Fairness-Accuracy Tradeoff with Few Unlabeled Test Samples}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29124},
}

@inproceedings{neurips2024_towards_70675,
  title = {{Towards Harmless Rawlsian Fairness Regardless of Demographic Prior}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96448},
}

@inproceedings{neurips2024_fair_71083,
  title = {{Fair Kernel K-Means: from Single Kernel to Multiple Kernel}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96130},
}

@inproceedings{neurips2024_fairness_73145,
  title = {{Fairness-Aware Meta-Learning via Nash Bargaining}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94286},
}

@inproceedings{neurips2025_secure_75583,
  title = {{Secure and Confidential Certificates of Online Fairness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=9jaQTx1O8T},
}

@inproceedings{aaai2025_who_11300,
  title = {{Who’s the (Multi-)Fairest of Them All: Rethinking Interpolation-Based Data Augmentation Through the Lens of Multicalibration}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33870},
}

@inproceedings{iclr2024_enhancing_33956,
  title = {{Enhancing Group Fairness in Online Settings Using Oblique Decision Forests}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19119},
}

@inproceedings{iclr2024_fair_35074,
  title = {{Fair Classifiers that Abstain without Harm}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17999},
}

@inproceedings{iclr2024_assessing_35126,
  title = {{Assessing Uncertainty in Similarity Scoring: Performance \& Fairness in Face Recognition}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17947},
}

@inproceedings{icml2024_frapp_44086,
  title = {{FRAPPÉ: A Group Fairness Framework for Post-Processing Everything}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34366},
}

@inproceedings{icml2024_how_44407,
  title = {{How Far Can Fairness Constraints Help Recover From Biased Data?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34045},
}

@inproceedings{icml2024_fair_44627,
  title = {{Fair Classification with Partial Feedback: An Exploration-Based Data Collection Approach}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33825},
}

@inproceedings{icml2025_fairicp_47446,
  title = {{FairICP: Encouraging Equalized Odds via Inverse Conditional Permutation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45147},
}