% Key papers for Private data analysis frameworks (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2018_scalable_28697,
  title = {{Scalable Private Learning with PATE}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/13},
}

@inproceedings{neurips2017_collecting_56200,
  title = {{Collecting Telemetry Data Privately}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9140},
}

@inproceedings{neurips2018_privacy_57529,
  title = {{Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11608},
}

@inproceedings{aaai2018_personalized_102447,
  title = {{Personalized Privacy-Preserving Social Recommendation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11714},
}

@inproceedings{neurips2020_auditing_59402,
  title = {{Auditing Differentially Private Machine Learning: How Private is Private SGD?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17094},
}

@inproceedings{cvpr2020_private_16062,
  title = {{Private-kNN: Practical Differential Privacy for Computer Vision}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{aaai2020_interpretable_101966,
  title = {{Interpretable and Differentially Private Predictions}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5827},
}

@inproceedings{aaai2021_differentially_433,
  title = {{Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/09932-differentially-private-and-fair-deep-learning-a-lagrangian-dual-approach/},
}

@inproceedings{icml2016_differentially_35794,
  title = {{Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/rogers16.html},
}

@inproceedings{icml2019_bounding_37197,
  title = {{Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4220},
}

@inproceedings{aaai2021_bias_198,
  title = {{Bias and Variance of Post-processing in Differential Privacy}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/11177-bias-and-variance-of-post-processing-in-differential-privacy/},
}

@inproceedings{aaai2018_geographic_100321,
  title = {{Geographic Differential Privacy for Mobile Crowd Coverage Maximization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11285},
}

@inproceedings{aaai2016_logical_104951,
  title = {{Logical Foundations of Privacy-Preserving Publishing of Linked Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10105},
}

@inproceedings{aaai2018_comparing_100337,
  title = {{Comparing Population Means Under Local Differential Privacy: With Significance and Power}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11301},
}

@inproceedings{neurips2019_differentially_58133,
  title = {{Differentially Private Bayesian Linear Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13289},
}

@inproceedings{neurips2018_local_57344,
  title = {{Local Differential Privacy for Evolving Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11247},
}

@inproceedings{neurips2016_differential_55679,
  title = {{Differential Privacy without Sensitivity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7233},
}

@inproceedings{icml2020_private_38638,
  title = {{Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6134},
}

@inproceedings{aaai2020_pairwise_100238,
  title = {{Pairwise Learning with Differential Privacy Guarantees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5411},
}

@inproceedings{icml2018_local_36830,
  title = {{Local Private Hypothesis Testing: Chi-Square Tests}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2346},
}

@inproceedings{aaai2025_federated_9185,
  title = {{Federated t-SNE and UMAP for Distributed Data Visualization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34204},
}

@inproceedings{iclr2024_one_33454,
  title = {{One-shot Empirical Privacy Estimation for Federated Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19621},
}

@inproceedings{aaai2024_protect_7019,
  title = {{Protect Your Score: Contact-Tracing with Differential Privacy Guarantees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29402},
}

@inproceedings{aaai2024_finding_6016,
  title = {{Finding ε and δ of Traditional Disclosure Control Systems}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30204},
}

@inproceedings{aaai2025_federated_9172,
  title = {{Federated Assemblies}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33520},
}

@inproceedings{aaai2025_fairness_9135,
  title = {{Fairness Issues and Mitigations in (Differentially Private) Socio-Demographic Data Processes}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35035},
}

@inproceedings{icml2025_differentially_48021,
  title = {{Differentially Private Boxplots}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44585},
}

@inproceedings{neurips2024_differentially_72873,
  title = {{Differentially Private Graph Diffusion with Applications in Personalized PageRanks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94531},
}

@inproceedings{neurips2024_credit_73003,
  title = {{Credit Attribution and Stable Compression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94418},
}

@inproceedings{neurips2024_efficient_73654,
  title = {{Efficient and Private Marginal Reconstruction with Local Non-Negativity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93838},
}

@inproceedings{icml2024_privacy_43302,
  title = {{Privacy Preserving Adaptive Experiment Design}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35150},
}

@inproceedings{icml2024_unveiling_43441,
  title = {{Unveiling Privacy, Memorization, and Input Curvature Links}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35011},
}

@inproceedings{icml2024_private_43735,
  title = {{Private Truly-Everlasting Robust-Prediction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34717},
}

@inproceedings{icml2024_optimal_44244,
  title = {{Optimal Differentially Private Model Training with Public Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34208},
}

@inproceedings{icml2024_privacy_45070,
  title = {{Privacy Profiles for Private Selection}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33382},
}

@inproceedings{icml2024_differentially_45211,
  title = {{Differentially Private Domain Adaptation with Theoretical Guarantees}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33241},
}

@inproceedings{icml2025_privacy_47417,
  title = {{Privacy Amplification Through Synthetic Data: Insights from Linear Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45176},
}

@inproceedings{icml2025_improving_49113,
  title = {{Improving the Variance of Differentially Private Randomized Experiments through Clustering}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43512},
}

@inproceedings{icml2026_accuracy_50451,
  title = {{Accuracy-First Rényi Differential Privacy and Post-Processing Immunity}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=CVDEc0doW8},
}

@inproceedings{icml2026_differentially_54251,
  title = {{Differentially Private Geodesic Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=nGlwpZFZre},
}