% Key papers for Likelihood-free and graphical model estimation (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2017_density_28280,
  title = {{Density estimation using Real NVP}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=HkpbnH9lx},
}

@inproceedings{cvpr2019_hierarchical_14281,
  title = {{Hierarchical Discrete Distribution Decomposition for Match Density Estimation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{icml2016_a_35716,
  title = {{A Kernelized Stein Discrepancy for Goodness-of-fit Tests}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/liub16.html},
}

@inproceedings{icml2019_a_37114,
  title = {{A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3795},
}

@inproceedings{neurips2020_modeling_60343,
  title = {{Modeling Noisy Annotations for Crowd Counting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18484},
}

@inproceedings{cvpr2020_voldor_16433,
  title = {{VOLDOR: Visual Odometry From Log-Logistic Dense Optical Flow Residuals}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{icml2017_high_36219,
  title = {{High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/737},
}

@inproceedings{icml2017_a_36040,
  title = {{A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/811},
}

@inproceedings{neurips2018_gaussian_57155,
  title = {{Gaussian Process Conditional Density Estimation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11248},
}

@inproceedings{aaai2022_unsupervised_2915,
  title = {{Unsupervised Anomaly Detection by Robust Density Estimation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/04101-unsupervised-anomaly-detection-by-robust-density-estimation/},
}

@inproceedings{neurips2019_space_58998,
  title = {{Space and Time Efficient Kernel Density Estimation in High Dimensions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14466},
}

@inproceedings{neurips2017_estimation_56299,
  title = {{Estimation of the covariance structure of heavy-tailed distributions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9070},
}

@inproceedings{neurips2018_random_57546,
  title = {{Random Feature Stein Discrepancies}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11202},
}

@inproceedings{icml2018_goodness_36718,
  title = {{Goodness-of-Fit Testing for Discrete Distributions via Stein Discrepancy}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1894},
}

@inproceedings{icml2020_on_38526,
  title = {{On Contrastive Learning for Likelihood-free Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6219},
}

@inproceedings{icml2018_transformation_37062,
  title = {{Transformation Autoregressive Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2324},
}

@inproceedings{aaai2023_spearman_4720,
  title = {{Spearman Rank Correlation Screening for Ultrahigh-Dimensional Censored Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26204},
}

@inproceedings{icml2016_markov_35899,
  title = {{Markov-modulated Marked Poisson Processes for Check-in Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/pana16.html},
}

@inproceedings{neurips2020_estimation_59803,
  title = {{Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17434},
}

@inproceedings{iclr2021_sliced_30683,
  title = {{Sliced Kernelized Stein Discrepancy}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2803},
}

@inproceedings{aaai2024_effectiveness_5799,
  title = {{Effectiveness of Constant Stepsize in Markovian LSA and Statistical Inference}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30028},
}

@inproceedings{icml2024_a_43347,
  title = {{A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35105},
}

@inproceedings{aaai2025_conditional_8494,
  title = {{Conditional Diffusion Models Based Conditional Independence Testing}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34354},
}

@inproceedings{icml2024_identification_44706,
  title = {{Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33746},
}

@inproceedings{icml2024_parameter_45203,
  title = {{Parameter Estimation in DAGs from Incomplete Data via Optimal Transport}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33249},
}

@inproceedings{icml2024_estimating_45448,
  title = {{Estimating Unknown Population Sizes Using the Hypergeometric Distribution}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33003},
}

@inproceedings{icml2024_kernel_45681,
  title = {{Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameters}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32771},
}

@inproceedings{neurips2024_robust_71795,
  title = {{Robust group and simultaneous inferences for high-dimensional single index model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95500},
}

@inproceedings{neurips2024_hierarchical_72649,
  title = {{Hierarchical Hybrid Sliced Wasserstein: A Scalable Metric for Heterogeneous Joint Distributions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94735},
}

@inproceedings{iclr2024_conjnorm_33507,
  title = {{ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19568},
}

@inproceedings{iclr2024_counterfactual_35585,
  title = {{Counterfactual Density Estimation using Kernel Stein Discrepancies}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17488},
}

@inproceedings{icml2024_statistical_43786,
  title = {{Statistical Inference Under Constrained Selection Bias}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34666},
}

@inproceedings{icml2024_sobolev_44326,
  title = {{Sobolev Space Regularised Pre Density Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34126},
}

@inproceedings{icml2024_momentum_45342,
  title = {{Momentum Particle Maximum Likelihood}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33109},
}

@inproceedings{icml2025_projection_47048,
  title = {{Projection Pursuit Density Ratio Estimation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45537},
}

@inproceedings{icml2025_a_47609,
  title = {{A Unified View on Learning Unnormalized Distributions via Noise-Contrastive Estimation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44988},
}

@inproceedings{icml2025_learning_47844,
  title = {{Learning Survival Distributions with the Asymmetric Laplace Distribution}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44758},
}

@inproceedings{icml2025_testing_47979,
  title = {{Testing Conditional Mean Independence Using Generative Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44626},
}

@inproceedings{icml2025_a_48728,
  title = {{A Sample Efficient Conditional Independence Test in the Presence of Discretization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43894},
}

@inproceedings{icml2026_relative_53172,
  title = {{Relative Entropy Estimation in Function Space: Theory and Applications to Trajectory Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=cpKJ2GlnYT},
}