% Key papers for Posterior sampling with diffusion guidance (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2020_magsac_15852,
  title = {{MAGSAC++, a Fast, Reliable and Accurate Robust Estimator}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{icml2016_deep_35791,
  title = {{Deep Structured Energy Based Models for Anomaly Detection}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/zhai16.html},
}

@inproceedings{cvpr2019_overcoming_14610,
  title = {{Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{neurips2019_learning_58496,
  title = {{Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13661},
}

@inproceedings{cvpr2022_recdis_19684,
  title = {{RecDis-SNN: Rectifying Membrane Potential Distribution for Directly Training Spiking Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2020_total_16347,
  title = {{Total Deep Variation for Linear Inverse Problems}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{iclr2018_large_28421,
  title = {{Large Scale Optimal Transport and Mapping Estimation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/179},
}

@inproceedings{neurips2017_ensemble_56295,
  title = {{Ensemble Sampling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9110},
}

@inproceedings{icml2019_conditioning_37246,
  title = {{Conditioning by adaptive sampling for robust design}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4261},
}

@inproceedings{neurips2021_equilibrium_61615,
  title = {{Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27700},
}

@inproceedings{icml2020_optimal_38589,
  title = {{Optimal transport mapping via input convex neural networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6233},
}

@inproceedings{icml2020_learning_38401,
  title = {{Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6649},
}

@inproceedings{neurips2019_exact_58232,
  title = {{Exact sampling of determinantal point processes with sublinear time preprocessing}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14138},
}

@inproceedings{neurips2017_parallel_56562,
  title = {{Parallel Streaming Wasserstein Barycenters}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9051},
}

@inproceedings{icml2019_stochastic_37761,
  title = {{Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3878},
}

@inproceedings{neurips2019_generalized_58329,
  title = {{Generalized Sliced Wasserstein Distances}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13268},
}

@inproceedings{neurips2021_end_62559,
  title = {{End-to-end reconstruction meets data-driven regularization for inverse problems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27648},
}

@inproceedings{icml2020_fully_38237,
  title = {{Fully Parallel Hyperparameter Search: Reshaped Space-Filling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6020},
}

@inproceedings{neurips2017_variational_56768,
  title = {{Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9218},
}

@inproceedings{icml2016_partition_35946,
  title = {{Partition Functions from Rao-Blackwellized Tempered Sampling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/carlson16.html},
}

@inproceedings{icml2024_a_45720,
  title = {{A Computational Framework for Solving Wasserstein Lagrangian Flows}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32732},
}

@inproceedings{icml2024_sample_43535,
  title = {{Sample as you Infer: Predictive Coding with Langevin Dynamics}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34917},
}

@inproceedings{icml2024_sampling_45515,
  title = {{Sampling in Unit Time with Kernel Fisher-Rao Flow}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32937},
}

@inproceedings{icml2025_sampling_46061,
  title = {{Sampling Binary Data by Denoising through Score Functions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46496},
}

@inproceedings{icml2024_iterated_45030,
  title = {{Iterated Denoising Energy Matching for Sampling from Boltzmann Densities}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33422},
}

@inproceedings{icml2024_minimizing_45171,
  title = {{Minimizing \$f\$-Divergences by Interpolating Velocity Fields}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33281},
}

@inproceedings{icml2025_a_47355,
  title = {{A Theoretical Framework For Overfitting In Energy-based Modeling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45237},
}

@inproceedings{neurips2024_hamiltonian_71542,
  title = {{Hamiltonian Score Matching and Generative Flows}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95722},
}

@inproceedings{iclr2025_neural_82251,
  title = {{Neural Sampling from Boltzmann Densities: Fisher-Rao Curves in the Wasserstein Geometry}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29539},
}

@inproceedings{aaai2025_exponential_9091,
  title = {{Exponential-Family Harmoniums with Neural Sufficient Statistics}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33677},
}

@inproceedings{iclr2024_sliced_34487,
  title = {{Sliced Wasserstein Estimation with Control Variates}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18587},
}

@inproceedings{iclr2024_entropy_35246,
  title = {{Entropy-MCMC: Sampling from Flat Basins with Ease}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17827},
}

@inproceedings{icml2024_improving_44096,
  title = {{Improving Gradient-Guided Nested Sampling for Posterior Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34356},
}

@inproceedings{icml2024_path_44129,
  title = {{Path-Guided Particle-based Sampling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34323},
}

@inproceedings{icml2024_sliced_44659,
  title = {{Sliced Wasserstein with Random-Path Projecting Directions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33793},
}

@inproceedings{icml2024_nonlinear_44828,
  title = {{Nonlinear Filtering with Brenier Optimal Transport Maps}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33624},
}

@inproceedings{icml2025_the_45962,
  title = {{The Polynomial Stein Discrepancy for Assessing Moment Convergence}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46584},
}

@inproceedings{icml2025_density_46761,
  title = {{Density Ratio Estimation with Conditional Probability Paths}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45812},
}

@inproceedings{icml2025_nets_47269,
  title = {{NETS: A Non-equilibrium Transport Sampler}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45320},
}

@inproceedings{icml2025_categorical_47299,
  title = {{Categorical Schrödinger Bridge Matching}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45290},
}