% Key papers for Variational inference theory and gradients (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2016_variational_36027,
  title = {{Variational Inference for Monte Carlo Objectives}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/mnihb16.html},
}

@inproceedings{neurips2017_hierarchical_56360,
  title = {{Hierarchical Implicit Models and Likelihood-Free Variational Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9327},
}

@inproceedings{icml2018_yes_37085,
  title = {{Yes, but Did It Work?: Evaluating Variational Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2055},
}

@inproceedings{neurips2016_r_55970,
  title = {{Rényi Divergence Variational Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7340},
}

@inproceedings{neurips2017_sticking_56687,
  title = {{Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9459},
}

@inproceedings{icml2017_variational_36448,
  title = {{Variational Boosting: Iteratively Refining Posterior Approximations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/558},
}

@inproceedings{neurips2016_operator_55916,
  title = {{Operator Variational Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7350},
}

@inproceedings{neurips2018_importance_57224,
  title = {{Importance Weighting and Variational Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11441},
}

@inproceedings{icml2019_understanding_37830,
  title = {{Understanding and Accelerating Particle-Based Variational Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3823},
}

@inproceedings{iclr2018_debiasing_28563,
  title = {{Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/114},
}

@inproceedings{icml2019_fast_37362,
  title = {{Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3799},
}

@inproceedings{icml2018_variational_37075,
  title = {{Variational Inference and Model Selection with Generalized Evidence Bounds}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2377},
}

@inproceedings{icml2018_black_36552,
  title = {{Black-Box Variational Inference for Stochastic Differential Equations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2034},
}

@inproceedings{icml2018_semi_36982,
  title = {{Semi-Implicit Variational Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2124},
}

@inproceedings{icml2019_a_37094,
  title = {{A Contrastive Divergence for Combining Variational Inference and MCMC}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3854},
}

@inproceedings{neurips2020_decision_59645,
  title = {{Decision-Making with Auto-Encoding Variational Bayes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16854},
}

@inproceedings{neurips2018_improving_57228,
  title = {{Improving Explorability in Variational Inference with Annealed Variational Objectives}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11922},
}

@inproceedings{iclr2019_variational_29033,
  title = {{Variational Bayesian Phylogenetic Inference}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1060},
}

@inproceedings{neurips2019_universal_59145,
  title = {{Universal Boosting Variational Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13522},
}

@inproceedings{icml2016_hierarchical_35866,
  title = {{Hierarchical Variational Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/ranganath16.html},
}

@inproceedings{aaai2025_denoising_8690,
  title = {{Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34194},
}

@inproceedings{aaai2024_generalized_6120,
  title = {{Generalized Variational Inference via Optimal Transport}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29035},
}

@inproceedings{icml2024_provably_43268,
  title = {{Provably Scalable Black-Box Variational Inference with Structured Variational Families}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35184},
}

@inproceedings{neurips2024_globally_70791,
  title = {{Globally Convergent Variational Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96338},
}

@inproceedings{neurips2024_visa_73673,
  title = {{VISA: Variational Inference with Sequential Sample-Average Approximations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93819},
}

@inproceedings{iclr2024_forward_34072,
  title = {{Forward \$chi^2\$ Divergence Based Variational Importance Sampling}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19003},
}

@inproceedings{icml2024_efficient_43968,
  title = {{Efficient Mixture Learning in Black-Box Variational Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34484},
}

@inproceedings{icml2024_batch_44830,
  title = {{Batch and match: black-box variational inference with a score-based divergence}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33622},
}

@inproceedings{icml2024_theoretical_45072,
  title = {{Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33380},
}

@inproceedings{icml2024_kernel_45691,
  title = {{Kernel Semi-Implicit Variational Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32761},
}

@inproceedings{icml2025_revisiting_46701,
  title = {{Revisiting Unbiased Implicit Variational Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45871},
}

@inproceedings{icml2025_variational_46884,
  title = {{Variational Learning of Fractional Posteriors}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45695},
}

@inproceedings{icml2025_censor_48075,
  title = {{Censor Dependent Variational Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44532},
}

@inproceedings{neurips2024_treevi_72711,
  title = {{TreeVI: Reparameterizable Tree-structured Variational Inference for Instance-level Correlation Capturing}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94676},
}

@inproceedings{neurips2024_particle_73927,
  title = {{Particle Semi-Implicit Variational Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93584},
}

@inproceedings{neurips2024_eigenvi_74237,
  title = {{EigenVI: score-based variational inference with orthogonal function expansions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93317},
}

@inproceedings{neurips2025_nearly_75984,
  title = {{Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=EKx21Vonwo},
}

@inproceedings{neurips2025_least_76227,
  title = {{Least squares variational inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Gvh6sU0uUt},
}

@inproceedings{neurips2025_hot_80145,
  title = {{HoT-VI: Reparameterizable Variational Inference for Capturing Instance-Level High-Order Correlations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=wdI2WKCN3P},
}

@inproceedings{neurips2025_fisher_80304,
  title = {{Fisher meets Feynman: score-based variational inference with a product of experts}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=yG8vmj3EAU},
}