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

@inproceedings{neurips2018_bayesian_56902,
  title = {{Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11056},
}

@inproceedings{aaai2019_learning_101635,
  title = {{Learning Plackett-Luce Mixtures from Partial Preferences}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4342},
}

@inproceedings{neurips2018_dimensionally_57045,
  title = {{Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11585},
}

@inproceedings{iclr2018_generalizing_28419,
  title = {{Generalizing Hamiltonian Monte Carlo with Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/284},
}

@inproceedings{neurips2022_sampling_66237,
  title = {{Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54096},
}

@inproceedings{aaai2016_exact_105363,
  title = {{Exact Sampling with Integer Linear Programs and Random Perturbations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10421},
}

@inproceedings{neurips2018_geometrically_57173,
  title = {{Geometrically Coupled Monte Carlo Sampling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11046},
}

@inproceedings{icml2016_hawkes_35861,
  title = {{Hawkes Processes with Stochastic Excitations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/leea16.html},
}

@inproceedings{neurips2016_fast_55726,
  title = {{Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7228},
}

@inproceedings{cvpr2018_fast_13099,
  title = {{Fast Monte-Carlo Localization on Aerial Vehicles Using Approximate Continuous Belief Representations}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Dhawale_Fast_Monte-Carlo_Localization_CVPR_2018_paper.html},
}

@inproceedings{cvpr2022_super_19959,
  title = {{Super-Fibonacci Spirals: Fast, Low-Discrepancy Sampling of SO(3)}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{neurips2017_dynamic_56272,
  title = {{Dynamic Importance Sampling for Anytime Bounds of the Partition Function}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9104},
}

@inproceedings{neurips2016_towards_56056,
  title = {{Towards Unifying Hamiltonian Monte Carlo and Slice Sampling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7232},
}

@inproceedings{neurips2018_on_57459,
  title = {{On Learning Markov Chains}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11087},
}

@inproceedings{iclr2019_auxiliary_29115,
  title = {{Auxiliary Variational MCMC}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/996},
}

@inproceedings{icml2020_batch_37962,
  title = {{Batch Stationary Distribution Estimation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6311},
}

@inproceedings{neurips2016_on_55903,
  title = {{On Mixtures of Markov Chains}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7321},
}

@inproceedings{icml2020_a_37871,
  title = {{A Generative Model for Molecular Distance Geometry}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6383},
}

@inproceedings{aaai2016_closed_105361,
  title = {{Closed-Form Gibbs Sampling for Graphical Models with Algebraic Constraints}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10419},
}

@inproceedings{icml2017_zonotope_36462,
  title = {{Zonotope Hit-and-run for Efficient Sampling from Projection DPPs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/680},
}

@inproceedings{iclr2024_protein_35687,
  title = {{Protein Discovery with Discrete Walk-Jump Sampling}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17386},
}

@inproceedings{neurips2024_constrained_73865,
  title = {{Constrained Sampling with Primal-Dual Langevin Monte Carlo}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93644},
}

@inproceedings{icml2024_beyond_44991,
  title = {{Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33461},
}

@inproceedings{icml2025_monte_46559,
  title = {{Monte-Carlo Tree Search with Uncertainty Propagation via Optimal Transport}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46010},
}

@inproceedings{neurips2024_hamiltonian_72818,
  title = {{Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficient}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94581},
}

@inproceedings{neurips2025_on_77829,
  title = {{On scalable and efficient training of diffusion samplers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Xzabk07lao},
}

@inproceedings{iclr2024_transport_34367,
  title = {{Transport meets Variational Inference: Controlled Monte Carlo Diffusions}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18708},
}

@inproceedings{iclr2024_reverse_35090,
  title = {{Reverse Diffusion Monte Carlo}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17983},
}

@inproceedings{iclr2024_chain_35665,
  title = {{Chain of Log-Concave Markov Chains}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17408},
}

@inproceedings{icml2024_diffusive_44360,
  title = {{Diffusive Gibbs Sampling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34092},
}

@inproceedings{icml2024_parallel_44431,
  title = {{Parallel Affine Transformation Tuning of Markov Chain Monte Carlo}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34021},
}

@inproceedings{icml2024_ai_45729,
  title = {{Ai-sampler: Adversarial Learning of Markov kernels with involutive maps}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32723},
}

@inproceedings{icml2025_beyond_45884,
  title = {{Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46659},
}

@inproceedings{icml2025_neural_46069,
  title = {{Neural Guided Diffusion Bridges}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46489},
}

@inproceedings{icml2025_leaps_46814,
  title = {{LEAPS: A discrete neural sampler via locally equivariant networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45761},
}

@inproceedings{icml2025_temperature_47329,
  title = {{Temperature-Annealed Boltzmann Generators}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45260},
}

@inproceedings{icml2025_sampling_48947,
  title = {{Sampling from Binary Quadratic Distributions via Stochastic Localization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43676},
}

@inproceedings{icml2026_score_51739,
  title = {{Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=PN8EiOzMuT},
}

@inproceedings{icml2026_efficient_51767,
  title = {{Efficient Online Variational Estimation via Monte Carlo Sampling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Ph2BaszK9w},
}

@inproceedings{icml2026_simple_52188,
  title = {{Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=TWTZ2kmojU},
}