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

@inproceedings{icml2017_measuring_36283,
  title = {{Measuring Sample Quality with Kernels}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/526},
}

@inproceedings{icml2017_probabilistic_36347,
  title = {{Probabilistic Path Hamiltonian Monte Carlo}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/615},
}

@inproceedings{iclr2019_probabilistic_28827,
  title = {{Probabilistic Planning with Sequential Monte Carlo methods}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/783},
}

@inproceedings{icml2017_magnetic_36279,
  title = {{Magnetic Hamiltonian Monte Carlo}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/508},
}

@inproceedings{aaai2021_bayesian_190,
  title = {{Bayesian Optimized Monte Carlo Planning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/11880-bayesian-optimized-monte-carlo-planning/},
}

@inproceedings{icml2016_ensuring_35817,
  title = {{Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/sa16.html},
}

@inproceedings{iclr2019_meta_28933,
  title = {{Meta-Learning For Stochastic Gradient MCMC}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1074},
}

@inproceedings{neurips2016_measuring_55868,
  title = {{Measuring the reliability of MCMC inference with bidirectional Monte Carlo}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7388},
}

@inproceedings{neurips2018_beyond_56909,
  title = {{Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11753},
}

@inproceedings{icml2017_fractional_36199,
  title = {{Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for Markov Chain Monte Carlo}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/462},
}

@inproceedings{icml2018_quasi_36944,
  title = {{Quasi-Monte Carlo Variational Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2288},
}

@inproceedings{neurips2022_variational_64597,
  title = {{Variational inference via Wasserstein gradient flows}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/55021},
}

@inproceedings{aaai2018_counting_101105,
  title = {{Counting Linear Extensions in Practice: MCMC Versus Exponential Monte Carlo}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11528},
}

@inproceedings{icml2016_interacting_35873,
  title = {{Interacting Particle Markov Chain Monte Carlo}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/rainforth16.html},
}

@inproceedings{neurips2019_estimating_58225,
  title = {{Estimating Convergence of Markov chains with L-Lag Couplings}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13826},
}

@inproceedings{neurips2019_gradient_58348,
  title = {{Gradient-based Adaptive Markov Chain Monte Carlo}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14460},
}

@inproceedings{neurips2016_scan_55985,
  title = {{Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7098},
}

@inproceedings{neurips2020_hamiltonian_59987,
  title = {{Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18933},
}

@inproceedings{icml2018_minibatch_36849,
  title = {{Minibatch Gibbs Sampling on Large Graphical Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2383},
}

@inproceedings{aaai2019_scaling_101652,
  title = {{Scaling-Up Split-Merge MCMC with Locality Sensitive Sampling (LSS)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4359},
}

@inproceedings{neurips2024_markovian_72871,
  title = {{Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94533},
}

@inproceedings{iclr2025_multilevel_82542,
  title = {{Multilevel Generative Samplers for Investigating Critical Phenomena}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29254},
}

@inproceedings{iclr2024_accelerated_35104,
  title = {{Accelerated Sampling with Stacked Restricted Boltzmann Machines}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17969},
}

@inproceedings{icml2024_learning_44758,
  title = {{Learning to Explore for Stochastic Gradient MCMC}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33694},
}

@inproceedings{icml2025_power_46987,
  title = {{Power Mean Estimation in Stochastic Continuous Monte-Carlo Tree Search}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45596},
}

@inproceedings{icml2025_autostep_47249,
  title = {{AutoStep: Locally adaptive involutive MCMC}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45340},
}

@inproceedings{icml2026_can_50507,
  title = {{Can Microcanonical Langevin Dynamics Leverage Mini-Batch Gradient Noise?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=D2evvc90tF},
}

@inproceedings{icml2026_practical_50593,
  title = {{Practical and Scalable Hamiltonian Monte Carlo Without the Metropolis Test}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=DxvZnTVNfP},
}

@inproceedings{icml2026_markov_53206,
  title = {{Markov Chain Monte Carlo without Evaluating the Target: an Auxiliary Variable Approach}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=dDkl5ZcyTl},
}

@inproceedings{neurips2024_functional_73622,
  title = {{Functional Gradient Flows for Constrained Sampling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93866},
}

@inproceedings{neurips2024_hamiltonian_74289,
  title = {{Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93269},
}

@inproceedings{neurips2025_parallelizing_77081,
  title = {{Parallelizing MCMC Across the Sequence Length}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=QOjUNzOkRN},
}

@inproceedings{neurips2025_metropolis_77834,
  title = {{Metropolis Adjusted Microcanonical Hamiltonian Monte Carlo}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Y38oEwTLTQ},
}

@inproceedings{iclr2025_parameter_82900,
  title = {{Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28902},
}

@inproceedings{iclr2026_accelerated_85263,
  title = {{Accelerated Parallel Tempering via Neural Transports}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=CODnlyYUli},
}

@inproceedings{neurips2026_contour_95399,
  title = {{Contour Monte Carlo: Sampling via Energy Level Sets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/155748},
}