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

@inproceedings{aaai2021_tempered_1475,
  title = {{Tempered Sigmoid Activations for Deep Learning with Differential Privacy}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/09312-tempered-sigmoid-activations-for-deep-learning-with-differential-privacy/},
}

@inproceedings{iclr2021_differentially_30408,
  title = {{Differentially Private Learning Needs Better Features (or Much More Data)}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3021},
}

@inproceedings{neurips2019_differential_58129,
  title = {{Differential Privacy Has Disparate Impact on Model Accuracy}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14443},
}

@inproceedings{aaai2020_privacy_100249,
  title = {{Privacy-Preserving Gradient Boosting Decision Trees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5422},
}

@inproceedings{aaai2020_differentially_100229,
  title = {{Differentially Private and Fair Classification via Calibrated Functional Mechanism}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5402},
}

@inproceedings{cvpr2021_scalable_17819,
  title = {{Scalable Differential Privacy With Sparse Network Finetuning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{neurips2017_differentially_56255,
  title = {{Differentially private Bayesian learning on distributed data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9107},
}

@inproceedings{aaai2022_dpnas_1919,
  title = {{DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/06358-dpnas-neural-architecture-search-for-deep-learning-with-differential-privacy/},
}

@inproceedings{cvpr2022_mixed_19347,
  title = {{Mixed Differential Privacy in Computer Vision}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{iclr2021_do_30034,
  title = {{Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2834},
}

@inproceedings{cvpr2023_learning_21402,
  title = {{Learning To Generate Image Embeddings With User-Level Differential Privacy}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21309},
}

@inproceedings{aaai2020_differentially_102226,
  title = {{Differentially Private Learning with Small Public Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6088},
}

@inproceedings{neurips2021_differentially_61424,
  title = {{Differentially Private Empirical Risk Minimization under the Fairness Lens}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26962},
}

@inproceedings{neurips2019_differentially_58136,
  title = {{Differentially Private Markov Chain Monte Carlo}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14521},
}

@inproceedings{icml2020_bayesian_37963,
  title = {{Bayesian Differential Privacy for Machine Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6547},
}

@inproceedings{icml2024_position_45338,
  title = {{Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33114},
}

@inproceedings{neurips2022_when_64398,
  title = {{When Does Differentially Private Learning Not Suffer in High Dimensions?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52951},
}

@inproceedings{iclr2023_disparate_33218,
  title = {{Disparate Impact in Differential Privacy from Gradient Misalignment}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/12111},
}

@inproceedings{neurips2023_privacy_68939,
  title = {{Privacy Auditing with One (1) Training Run}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70925},
}

@inproceedings{icml2021_accuracy_39012,
  title = {{Accuracy, Interpretability, and Differential Privacy via Explainable Boosting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9575},
}

@inproceedings{aaai2024_wasserstein_7784,
  title = {{Wasserstein Differential Privacy}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29565},
}

@inproceedings{cvpr2024_in_23790,
  title = {{In-distribution Public Data Synthesis with Diffusion Models for Differentially Private Image Classification}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31276},
}

@inproceedings{icml2024_differentially_45001,
  title = {{Differentially Private Bias-Term Fine-tuning of Foundation Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33451},
}

@inproceedings{icml2024_differentially_44267,
  title = {{Differentially Private Representation Learning via Image Captioning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34185},
}

@inproceedings{aaai2025_privacy_10356,
  title = {{Privacy, Utility and Fairness: Navigating Trade-offs in Differentially Private Machine Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35205},
}

@inproceedings{neurips2025_unifying_79668,
  title = {{Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=rem4dgVrFg},
}

@inproceedings{aaai2024_a_5066,
  title = {{A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30153},
}

@inproceedings{iclr2024_confidential_34368,
  title = {{Confidential-DPproof: Confidential Proof of Differentially Private Training}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18707},
}

@inproceedings{icml2024_privacy_43454,
  title = {{Privacy-Preserving Data Release Leveraging Optimal Transport and Particle Gradient Descent}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34998},
}

@inproceedings{icml2024_delving_43935,
  title = {{Delving into Differentially Private Transformer}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34517},
}

@inproceedings{icml2024_shifted_44101,
  title = {{Shifted Interpolation for Differential Privacy}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34351},
}

@inproceedings{neurips2024_privacy_70385,
  title = {{Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96715},
}

@inproceedings{neurips2024_revisiting_70396,
  title = {{Revisiting Differentially Private ReLU Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96706},
}

@inproceedings{neurips2024_unified_73746,
  title = {{Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93755},
}

@inproceedings{iclr2025_tighter_84072,
  title = {{Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27756},
}

@inproceedings{iclr2024_correlated_35614,
  title = {{Correlated Noise Provably Beats Independent Noise for Differentially Private Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17459},
}

@inproceedings{iclr2024_privacy_35621,
  title = {{Privacy Amplification for Matrix Mechanisms}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17452},
}

@inproceedings{icml2024_the_43467,
  title = {{The Privacy Power of Correlated Noise in Decentralized Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34985},
}

@inproceedings{icml2024_neural_43584,
  title = {{Neural Collapse meets Differential Privacy: Curious behaviors of NoisyGD with Near-Perfect Representation Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34868},
}