% Key papers for Sequential Monte Carlo for inference (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2020_on_102111,
  title = {{On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5973},
}

@inproceedings{iclr2018_auto_28423,
  title = {{Auto-Encoding Sequential Monte Carlo}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/31},
}

@inproceedings{icml2017_learning_36250,
  title = {{Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/876},
}

@inproceedings{cvpr2019_divergence_14100,
  title = {{Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{icml2016_inference_35872,
  title = {{Inference Networks for Sequential Monte Carlo in Graphical Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/paige16.html},
}

@inproceedings{neurips2021_learning_61985,
  title = {{Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27657},
}

@inproceedings{cvpr2020_mcflow_15875,
  title = {{McFlow: Monte Carlo Flow Models for Data Imputation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{neurips2016_neurally_55894,
  title = {{Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7021},
}

@inproceedings{neurips2019_an_57893,
  title = {{An Adaptive Empirical  Bayesian Method for Sparse Deep Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13684},
}

@inproceedings{neurips2016_pairwise_55931,
  title = {{Pairwise Choice Markov Chains}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7044},
}

@inproceedings{icml2020_likelihood_38421,
  title = {{Likelihood-free MCMC with Amortized Approximate Ratio Estimators}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5834},
}

@inproceedings{neurips2018_meta_57371,
  title = {{Meta-Learning MCMC Proposals}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11411},
}

@inproceedings{aaai2021_learning_871,
  title = {{Learning Energy-Based Model with Variational Auto-Encoder as Amortized Sampler}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/10441-learning-energy-based-model-with-variational-auto-encoder-as-amortized-sampler/},
}

@inproceedings{icml2023_sampling_43117,
  title = {{Sampling-Based Accuracy Testing of Posterior Estimators for General Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24029},
}

@inproceedings{icml2016_diversity_35801,
  title = {{Diversity-Promoting Bayesian Learning of Latent Variable Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/xiea16.html},
}

@inproceedings{neurips2018_mental_57368,
  title = {{Mental Sampling in Multimodal Representations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11560},
}

@inproceedings{iclr2020_unbiased_29748,
  title = {{Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=r1eyceSYPr},
}

@inproceedings{icml2020_undirected_38902,
  title = {{Undirected Graphical Models as Approximate Posteriors}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5975},
}

@inproceedings{icml2021_monte_39627,
  title = {{Monte Carlo Variational Auto-Encoders}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8527},
}

@inproceedings{icml2019_beyond_37191,
  title = {{Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4022},
}

@inproceedings{aaai2024_using_7735,
  title = {{Using Stratified Sampling to Improve LIME Image Explanations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29397},
}

@inproceedings{cvpr2024_accelerating_22701,
  title = {{Accelerating Neural Field Training via Soft Mining}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29238},
}

@inproceedings{aaai2024_direct_5697,
  title = {{Direct Amortized Likelihood Ratio Estimation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30018},
}

@inproceedings{neurips2024_topological_70844,
  title = {{Topological Hidden Markov Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/98320},
}

@inproceedings{iclr2025_fast_80705,
  title = {{Fast training and sampling of Restricted Boltzmann Machines}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/31062},
}

@inproceedings{icml2024_probabilistic_45002,
  title = {{Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33450},
}

@inproceedings{icml2024_online_45158,
  title = {{Online Variational Sequential Monte Carlo}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33294},
}

@inproceedings{neurips2024_quest_73085,
  title = {{QUEST: Quality-Aware Metropolis-Hastings Sampling for Machine Translation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94343},
}

@inproceedings{neurips2024_an_73581,
  title = {{An Analysis of Elo Rating Systems via Markov Chains}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93904},
}

@inproceedings{neurips2025_psi_79768,
  title = {{\$Psi\$-Sampler: Initial Particle Sampling for SMC-Based Inference-Time Reward Alignment in Score Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=slVqJAI5sT},
}

@inproceedings{icml2024_model_45446,
  title = {{Model-Based Minimum Bayes Risk Decoding for Text Generation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33005},
}

@inproceedings{icml2025_learning_46142,
  title = {{Learning Extrapolative Sequence Transformations from Markov Chains}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46418},
}

@inproceedings{icml2025_beyond_48928,
  title = {{Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43695},
}

@inproceedings{icml2026_a_49498,
  title = {{A Probabilistic Framework for LLM-Based Model Discovery}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=356U11JaTS},
}

@inproceedings{icml2026_estimating_51216,
  title = {{Estimating Tail Risks in Language Model Output Distributions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Joka19sTny},
}

@inproceedings{icml2026_mitigating_51375,
  title = {{Mitigating Bias in Locally Constrained Decoding via Tractable Proposals}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=LYBs6f3jlK},
}

@inproceedings{icml2026_latent_53924,
  title = {{Latent Guided Sampling for Combinatorial Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=k5xsXvNYQS},
}

@inproceedings{icml2026_efficient_54172,
  title = {{Efficient Learning of Deep State Space Models via Importance Smoothing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=md3glg1Lft},
}

@inproceedings{neurips2025_constrained_76685,
  title = {{Constrained Sampling for Language Models Should Be Easy: An MCMC Perspective}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=M1b7IuY6Co},
}

@inproceedings{neurips2025_better_79973,
  title = {{Better Estimation of the Kullback--Leibler Divergence Between Language Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=um9kHMof0c},
}