% Key papers for Local-DP and private estimation (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2016_discrete_35798,
  title = {{Discrete Distribution Estimation under Local Privacy}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/kairouz16.html},
}

@inproceedings{aaai2016_wishart_105013,
  title = {{Wishart Mechanism for Differentially Private Principal Components Analysis}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10185},
}

@inproceedings{icml2019_on_37595,
  title = {{On Sparse Linear Regression in the Local Differential Privacy Model}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3642},
}

@inproceedings{neurips2018_differentially_57039,
  title = {{Differentially Private Testing of Identity and Closeness of Discrete Distributions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11663},
}

@inproceedings{aaai2016_on_105082,
  title = {{On the Differential Privacy of Bayesian Inference}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10254},
}

@inproceedings{icml2019_graphical_37410,
  title = {{Graphical-model based estimation and inference for differential privacy}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3639},
}

@inproceedings{icml2019_communication_37230,
  title = {{Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3570},
}

@inproceedings{neurips2020_coinpress_59546,
  title = {{CoinPress: Practical Private Mean and Covariance Estimation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16811},
}

@inproceedings{neurips2018_differentially_57040,
  title = {{Differentially Private Uniformly Most Powerful Tests for Binomial Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11417},
}

@inproceedings{neurips2019_average_57928,
  title = {{Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13263},
}

@inproceedings{neurips2018_differentially_57035,
  title = {{Differentially Private Bayesian Inference for Exponential Families}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11298},
}

@inproceedings{icml2021_leveraging_39561,
  title = {{Leveraging Public Data for Practical Private Query Release}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9383},
}

@inproceedings{neurips2019_locally_58557,
  title = {{Locally Private Gaussian Estimation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13481},
}

@inproceedings{icml2019_locally_37517,
  title = {{Locally Private Bayesian Inference for Count Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4109},
}

@inproceedings{icml2018_differentially_36639,
  title = {{Differentially Private Identity and Equivalence Testing of Discrete Distributions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2415},
}

@inproceedings{icml2022_improved_40683,
  title = {{Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16327},
}

@inproceedings{icml2017_priv_36346,
  title = {{Priv’IT: Private and Sample Efficient Identity Testing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/459},
}

@inproceedings{neurips2019_private_58838,
  title = {{Private Testing of Distributions via Sample Permutations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14089},
}

@inproceedings{neurips2021_covariance_61856,
  title = {{Covariance-Aware Private Mean Estimation Without Private Covariance Estimation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26983},
}

@inproceedings{neurips2021_instance_61572,
  title = {{Instance-optimal Mean Estimation Under Differential Privacy}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28592},
}

@inproceedings{neurips2024_exactly_71155,
  title = {{Exactly Minimax-Optimal Locally Differentially Private Sampling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96070},
}

@inproceedings{neurips2024_a_72343,
  title = {{A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95005},
}

@inproceedings{neurips2024_on_74621,
  title = {{On Differentially Private U Statistics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/92970},
}

@inproceedings{neurips2025_sum_75655,
  title = {{Sum Estimation under Personalized Local Differential Privacy}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=AXlquRUO0S},
}

@inproceedings{iclr2024_improved_34779,
  title = {{Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18295},
}

@inproceedings{icml2024_mean_44876,
  title = {{Mean Estimation in the Add-Remove Model of Differential Privacy}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33576},
}

@inproceedings{icml2024_better_45713,
  title = {{Better Locally Private Sparse Estimation Given Multiple Samples Per User}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32739},
}

@inproceedings{icml2025_optimal_46004,
  title = {{Optimal Survey Design for Private Mean Estimation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46542},
}

@inproceedings{icml2025_lightweight_49142,
  title = {{Lightweight Protocols for Distributed Private Quantile Estimation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43483},
}

@inproceedings{icml2026_accurate_49565,
  title = {{Accurate, Private, Secure, Federated U-statistics with Higher Degree}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=3i4xYPxtTf},
}

@inproceedings{icml2026_near_51127,
  title = {{Near-Optimal Private Linear Regression via Iterative Hessian Mixing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=IwP8LZzc97},
}

@inproceedings{icml2026_shuffling_53358,
  title = {{Shuffling-Aware Optimization for Private Vector Mean Estimation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=eldrXxp9eG},
}

@inproceedings{neurips2024_optimal_71063,
  title = {{Optimal Private and Communication Constraint Distributed Goodness-of-Fit Testing for Discrete Distributions in the Large Sample Regime}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96147},
}

@inproceedings{neurips2024_on_72050,
  title = {{On the Computational Complexity of Private High-dimensional Model Selection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95270},
}

@inproceedings{neurips2024_truthful_72782,
  title = {{Truthful High Dimensional Sparse Linear Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94613},
}

@inproceedings{neurips2025_when_74982,
  title = {{⁠When Data Can't Meet: Estimating Correlation Across Privacy Barriers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=36uy2GgAy6},
}

@inproceedings{neurips2025_a_76032,
  title = {{A Private Approximation of the 2nd-Moment Matrix of Any Subsamplable Input}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Ep4mYI7OLF},
}

@inproceedings{neurips2025_time_76774,
  title = {{Time-uniform and Asymptotic Confidence Sequence of Quantile under Local Differential Privacy}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Mt2UnV8jmB},
}

@inproceedings{neurips2025_an_77310,
  title = {{An Iterative Algorithm for Differentially Private \$k\$-PCA with Adaptive Noise}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=SWsDJbIloF},
}

@inproceedings{neurips2025_continual_77408,
  title = {{Continual Release Moment Estimation with Differential Privacy}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=TXHc1gEEIk},
}