% Key papers for Importance sampling and quadrature (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2016_a_35715,
  title = {{A Kernel Test of Goodness of Fit}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/chwialkowski16.html},
}

@inproceedings{neurips2016_fast_55728,
  title = {{Fast and Provably Good Seedings for k-Means}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7208},
}

@inproceedings{icml2018_on_36887,
  title = {{On Nesting Monte Carlo Estimators}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1874},
}

@inproceedings{icml2019_stein_37760,
  title = {{Stein Point Markov Chain Monte Carlo}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3730},
}

@inproceedings{icml2019_unifying_37837,
  title = {{Unifying Orthogonal Monte Carlo Methods}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3580},
}

@inproceedings{icml2016_importance_35870,
  title = {{Importance Sampling Tree for Large-scale Empirical Expectation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/canevet16.html},
}

@inproceedings{neurips2024_principled_72640,
  title = {{Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94741},
}

@inproceedings{icml2018_dice_36633,
  title = {{DiCE: The Infinitely Differentiable Monte Carlo Estimator}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2266},
}

@inproceedings{neurips2019_tensor_59054,
  title = {{Tensor Monte Carlo: Particle Methods for the GPU era}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13808},
}

@inproceedings{icml2019_adaptive_37130,
  title = {{Adaptive Antithetic Sampling for Variance Reduction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4125},
}

@inproceedings{icml2017_on_36323,
  title = {{On the Sampling Problem for Kernel Quadrature}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/695},
}

@inproceedings{neurips2020_demystifying_59682,
  title = {{Demystifying Orthogonal Monte Carlo and Beyond}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17102},
}

@inproceedings{icml2021_annealed_39054,
  title = {{Annealed Flow Transport Monte Carlo}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10537},
}

@inproceedings{icml2019_amortized_37151,
  title = {{Amortized Monte Carlo Integration}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3869},
}

@inproceedings{icml2023_vector_41394,
  title = {{Vector-Valued Control Variates}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24711},
}

@inproceedings{neurips2021_continuous_61467,
  title = {{Continuous-time edge modelling using non-parametric point processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27388},
}

@inproceedings{neurips2022_a_64714,
  title = {{A Quadrature Rule combining Control Variates and Adaptive Importance Sampling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54756},
}

@inproceedings{neurips2022_adaptive_65813,
  title = {{Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54167},
}

@inproceedings{neurips2023_efficient_67858,
  title = {{Efficient Training of Energy-Based Models Using Jarzynski Equality}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71904},
}

@inproceedings{neurips2024_automated_70310,
  title = {{Automated Efficient Estimation using Monte Carlo Efficient Influence Functions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96784},
}

@inproceedings{icml2024_sliced_43334,
  title = {{Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35118},
}

@inproceedings{aaai2026_monte_104650,
  title = {{Monte Carlo Diffusion for Generalizable Learning-Based RANSAC}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/37954},
}

@inproceedings{iclr2024_plug_33672,
  title = {{Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19403},
}

@inproceedings{icml2024_stochastic_43339,
  title = {{Stochastic Localization via Iterative Posterior Sampling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35113},
}

@inproceedings{icml2024_pasoa_45487,
  title = {{PASOA- PArticle baSed Bayesian Optimal Adaptive design}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32964},
}

@inproceedings{icml2025_nested_47137,
  title = {{Nested Expectations with Kernel Quadrature}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45450},
}

@inproceedings{icml2025_solving_47247,
  title = {{Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45342},
}

@inproceedings{icml2025_efficient_47888,
  title = {{Efficient Noise Calculation in Deep Learning-based MRI Reconstructions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44715},
}

@inproceedings{icml2026_entropic_50916,
  title = {{Entropic Mirror Monte Carlo}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=GugLtzm1WC},
}

@inproceedings{icml2026_neural_52911,
  title = {{Neural Quantum States in Mixed Precision}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=aDLJNoseDH},
}

@inproceedings{icml2026_efficient_53623,
  title = {{Efficient Stochastic Optimisation via Sequential Monte Carlo}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=hCIBCAS1Hi},
}

@inproceedings{neurips2024_fearless_70380,
  title = {{Fearless Stochasticity in Expectation Propagation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96720},
}

@inproceedings{neurips2024_energy_74398,
  title = {{Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93171},
}

@inproceedings{neurips2025_reverse_78937,
  title = {{Reverse-Annealed Sequential Monte Carlo for Efficient Bayesian Optimal Experiment Design}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=jut5q3UYRz},
}

@inproceedings{neurips2026_variance_91948,
  title = {{Variance Reduction for Expectations with Diffusion Teachers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/153246},
}

@inproceedings{iclr2024_monte_35209,
  title = {{Monte Carlo guided Denoising Diffusion models for Bayesian linear inverse problems.}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17864},
}

@inproceedings{iclr2024_fiber_35425,
  title = {{Fiber Monte Carlo}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17648},
}

@inproceedings{iclr2024_diffusion_35473,
  title = {{Diffusion Posterior Sampling for Linear Inverse Problem Solving: A Filtering Perspective}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17600},
}