% Key papers for Score-based generative modeling analysis (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2019_generative_58331,
  title = {{Generative Modeling by Estimating Gradients of the Data Distribution}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13242},
}

@inproceedings{icml2017_compressed_36103,
  title = {{Compressed Sensing using Generative Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/796},
}

@inproceedings{cvpr2020_effectively_15459,
  title = {{Effectively Unbiased FID and Inception Score and Where to Find Them}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{icml2016_a_35726,
  title = {{A Theory of Generative ConvNet}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/xiec16.html},
}

@inproceedings{neurips2018_assessing_56875,
  title = {{Assessing Generative Models via Precision and Recall}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11511},
}

@inproceedings{iclr2020_input_29733,
  title = {{Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=SyxIWpVYvr},
}

@inproceedings{neurips2018_generative_57170,
  title = {{Generative Probabilistic Novelty Detection with Adversarial Autoencoders}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11658},
}

@inproceedings{iclr2018_ambientgan_28556,
  title = {{AmbientGAN: Generative models from lossy measurements}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/231},
}

@inproceedings{neurips2017_wasserstein_56771,
  title = {{Wasserstein Learning of Deep Generative Point Process Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9109},
}

@inproceedings{neurips2018_bias_56910,
  title = {{Bias and Generalization in Deep Generative Models: An Empirical Study}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/12020},
}

@inproceedings{neurips2019_bias_57954,
  title = {{Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14102},
}

@inproceedings{iclr2018_gradient_28605,
  title = {{Gradient Estimators for Implicit Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/252},
}

@inproceedings{icml2018_pixelsnail_36922,
  title = {{PixelSNAIL: An Improved Autoregressive Generative Model}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2296},
}

@inproceedings{cvpr2020_bayesian_15187,
  title = {{Bayesian Adversarial Human Motion Synthesis}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{neurips2021_robust_63473,
  title = {{Robust Compressed Sensing MRI with Deep Generative Priors}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27649},
}

@inproceedings{aaai2020_structured_102078,
  title = {{Structured Output Learning with Conditional Generative Flows}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5940},
}

@inproceedings{aaai2018_boosted_102560,
  title = {{Boosted Generative Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11827},
}

@inproceedings{neurips2016_conditional_55639,
  title = {{Conditional Generative Moment-Matching Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7183},
}

@inproceedings{iclr2020_the_29544,
  title = {{The Shape of Data: Intrinsic Distance for Data Distributions}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=HyebplHYwB},
}

@inproceedings{cvpr2019_a_13774,
  title = {{A Generative Adversarial Density Estimator}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{neurips2024_estimating_71733,
  title = {{Estimating the Hallucination Rate of Generative AI}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95553},
}

@inproceedings{iclr2024_idempotent_34619,
  title = {{Idempotent Generative Network}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18455},
}

@inproceedings{aaai2024_qcs_7032,
  title = {{QCS-SGM+: Improved Quantized Compressed Sensing with Score-Based Generative Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29347},
}

@inproceedings{cvpr2025_unveiling_28030,
  title = {{Unveiling Differences in Generative Models: A Scalable Differential Clustering Approach}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34419},
}

@inproceedings{iclr2025_pqmass_83390,
  title = {{PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28426},
}

@inproceedings{aaai2024_robust_7171,
  title = {{Robust Evaluation Measures for Evaluating Social Biases in Masked Language Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29834},
}

@inproceedings{aaai2025_supervised_10874,
  title = {{Supervised Score-Based Modeling by Gradient Boosting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34437},
}

@inproceedings{iclr2024_multimarginal_34000,
  title = {{Multimarginal Generative Modeling with Stochastic Interpolants}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19075},
}

@inproceedings{iclr2024_score_35573,
  title = {{Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian distributions}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17500},
}

@inproceedings{icml2024_towards_44788,
  title = {{Towards Theoretical Understandings of Self-Consuming Generative Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33664},
}

@inproceedings{icml2024_an_45705,
  title = {{An Interpretable Evaluation of Entropy-based Novelty of Generative Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32747},
}

@inproceedings{aaai2025_a_7946,
  title = {{A Wiener Process Perspective on Local Intrinsic Dimension Estimation Methods}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34299},
}

@inproceedings{iclr2024_sampling_35243,
  title = {{Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17830},
}

@inproceedings{icml2024_generative_44646,
  title = {{Generative Marginalization Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33806},
}

@inproceedings{icml2024_brain_45350,
  title = {{BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33101},
}

@inproceedings{icml2024_learning_45357,
  title = {{Learning Latent Space Hierarchical EBM Diffusion Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33094},
}

@inproceedings{icml2025_a_45898,
  title = {{A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46645},
}

@inproceedings{icml2025_hessian_46781,
  title = {{Hessian Geometry of Latent Space in Generative Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45794},
}

@inproceedings{icml2025_a_47172,
  title = {{A Theory for Conditional Generative Modeling on Multiple Data Sources}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45415},
}

@inproceedings{icml2025_expressive_47538,
  title = {{Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving Regularization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45058},
}