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

@inproceedings{neurips2016_stochastic_56014,
  title = {{Stochastic Optimization for Large-scale Optimal Transport}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/8509},
}

@inproceedings{iclr2020_cyclical_29819,
  title = {{Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=rkeS1RVtPS},
}

@inproceedings{icml2018_not_36881,
  title = {{Not All Samples Are Created Equal: Deep Learning with Importance Sampling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2178},
}

@inproceedings{aaai2021_sample_1324,
  title = {{Sample Efficient Reinforcement Learning with REINFORCE}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/10887-sample-efficient-reinforcement-learning-with-reinforce/},
}

@inproceedings{neurips2018_training_57724,
  title = {{Training Deep Models Faster with Robust, Approximate Importance Sampling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11700},
}

@inproceedings{icml2016_a_35727,
  title = {{A Variational Analysis of Stochastic Gradient Algorithms}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/mandt16.html},
}

@inproceedings{neurips2018_the_57703,
  title = {{The promises and pitfalls of Stochastic Gradient Langevin Dynamics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11791},
}

@inproceedings{cvpr2016_learning_11671,
  title = {{Learning Weight Uncertainty With Stochastic Gradient MCMC for Shape Classification}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{icml2016_adaptive_35733,
  title = {{Adaptive Sampling for SGD by Exploiting Side Information}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/gopal16.html},
}

@inproceedings{icml2017_deep_36134,
  title = {{Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/703},
}

@inproceedings{neurips2019_information_58413,
  title = {{Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14098},
}

@inproceedings{aaai2019_active_101805,
  title = {{Active Mini-Batch Sampling Using Repulsive Point Processes}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4520},
}

@inproceedings{neurips2017_reducing_56617,
  title = {{Reducing Reparameterization Gradient Variance}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9153},
}

@inproceedings{icml2016_stochastic_35991,
  title = {{Stochastic Quasi-Newton Langevin Monte Carlo}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/simsekli16.html},
}

@inproceedings{neurips2018_stochastic_57654,
  title = {{Stochastic Expectation Maximization with Variance Reduction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11764},
}

@inproceedings{icml2016_a_35721,
  title = {{A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/curtis16.html},
}

@inproceedings{neurips2016_a_55535,
  title = {{A Multi-Batch L-BFGS Method for Machine Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/8504},
}

@inproceedings{icml2017_stochastic_36408,
  title = {{Stochastic Bouncy Particle Sampler}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/890},
}

@inproceedings{icml2017_adaptive_36054,
  title = {{Adaptive Sampling Probabilities for Non-Smooth Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/562},
}

@inproceedings{aaai2019_cogra_101668,
  title = {{Cogra: Concept-Drift-Aware Stochastic Gradient Descent for Time-Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4383},
}

@inproceedings{aaai2024_on_6824,
  title = {{On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental Design}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30012},
}

@inproceedings{icml2024_constrained_43816,
  title = {{Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34636},
}

@inproceedings{neurips2024_non_72352,
  title = {{Non-asymptotic Analysis of Biased Adaptive Stochastic Approximation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94997},
}

@inproceedings{aaai2025_coupling_8553,
  title = {{Coupling-based Convergence Diagnostic and Stepsize Scheme for Stochastic Gradient Descent}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34035},
}

@inproceedings{iclr2024_accelerating_33861,
  title = {{Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19214},
}

@inproceedings{iclr2024_stochastic_34899,
  title = {{Stochastic Gradient Descent for Gaussian Processes Done Right}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18175},
}

@inproceedings{neurips2024_gradient_70481,
  title = {{Gradient-based Discrete Sampling with Automatic Cyclical Scheduling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96627},
}

@inproceedings{iclr2024_ito_34901,
  title = {{Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18173},
}

@inproceedings{icml2024_understanding_45230,
  title = {{Understanding Stochastic Natural Gradient Variational Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33222},
}

@inproceedings{icml2024_demystifying_45806,
  title = {{Demystifying SGD with Doubly Stochastic Gradients}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32646},
}

@inproceedings{icml2025_fundamental_47020,
  title = {{Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45565},
}

@inproceedings{icml2025_tuning_47214,
  title = {{Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45373},
}

@inproceedings{icml2025_regularized_47866,
  title = {{Regularized Langevin Dynamics for Combinatorial Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44737},
}

@inproceedings{icml2026_svrg_49535,
  title = {{SVRG and Beyond via Posterior Correction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=3NQSeJOfkz},
}

@inproceedings{icml2026_flatness_51319,
  title = {{Flatness-Aware Stochastic Gradient Langevin Dynamics}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Kw0nhxeTeI},
}

@inproceedings{icml2026_gradient_51851,
  title = {{Gradient Flow Sampler-based Distributionally Robust Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=QRtzkKrbJi},
}

@inproceedings{icml2026_zeroth_52327,
  title = {{Zeroth-Order Non-Log-Concave Sampling with Variance Reduction and Applications to Inverse Problems}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=UlFOINq6SY},
}

@inproceedings{icml2026_accurate_52865,
  title = {{Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Zkj9ctQdMM},
}

@inproceedings{icml2026_robust_54204,
  title = {{Robust Stochastic Gradient Posterior Sampling with Lattice Based Discretisation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=mtRgrp0u7Z},
}

@inproceedings{neurips2024_random_74534,
  title = {{Random Function Descent}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93048},
}