% Key papers for Sparse variational Gaussian processes (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2018_gpytorch_57152,
  title = {{GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11728},
}

@inproceedings{neurips2016_stochastic_56018,
  title = {{Stochastic Variational Deep Kernel Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/6960},
}

@inproceedings{icml2016_structured_35999,
  title = {{Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/louizos16.html},
}

@inproceedings{neurips2017_convolutional_56220,
  title = {{Convolutional Gaussian Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9069},
}

@inproceedings{neurips2017_doubly_56265,
  title = {{Doubly Stochastic Variational Inference for Deep Gaussian Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9236},
}

@inproceedings{icml2019_rates_37686,
  title = {{Rates of Convergence for Sparse Variational Gaussian Process Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3737},
}

@inproceedings{neurips2017_variational_56764,
  title = {{Variational Inference for Gaussian Process Models with Linear Complexity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9294},
}

@inproceedings{neurips2017_learning_56436,
  title = {{Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9340},
}

@inproceedings{icml2016_a_35714,
  title = {{A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/hoang16.html},
}

@inproceedings{neurips2023_gauche_69875,
  title = {{GAUCHE: A Library for Gaussian Processes in Chemistry}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70081},
}

@inproceedings{aaai2017_multivariate_104469,
  title = {{Multivariate Hawkes Processes for Large-Scale Inference}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10846},
}

@inproceedings{aaai2017_a_104395,
  title = {{A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10772},
}

@inproceedings{neurips2017_scalable_56652,
  title = {{Scalable Log Determinants for Gaussian Process Kernel Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9403},
}

@inproceedings{cvpr2023_robust_22080,
  title = {{Robust and Scalable Gaussian Process Regression and Its Applications}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21715},
}

@inproceedings{neurips2017_excess_56300,
  title = {{Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9291},
}

@inproceedings{aaai2020_scalable_102199,
  title = {{Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6061},
}

@inproceedings{icml2020_bayesian_37966,
  title = {{Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6407},
}

@inproceedings{icml2021_global_39394,
  title = {{Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9139},
}

@inproceedings{aaai2019_efficient_101766,
  title = {{Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4481},
}

@inproceedings{icml2020_sparse_38772,
  title = {{Sparse Gaussian Processes with Spherical Harmonic Features}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5898},
}

@inproceedings{aaai2024_sparse_7378,
  title = {{Sparse Variational Student-t Processes}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29549},
}

@inproceedings{aaai2024_domain_5742,
  title = {{Domain Invariant Learning for Gaussian Processes and Bayesian Exploration}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29646},
}

@inproceedings{neurips2024_numerically_70847,
  title = {{Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/98323},
}

@inproceedings{neurips2026_sublinear_89586,
  title = {{Sublinear Variational Optimization of Gaussian Mixture Models with Millions to Billions of Parameters}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/156665},
}

@inproceedings{icml2024_variational_43317,
  title = {{Variational Linearized Laplace Approximation for Bayesian Deep Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35135},
}

@inproceedings{icml2025_new_47219,
  title = {{New Bounds for Sparse Variational Gaussian Processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45368},
}

@inproceedings{icml2025_k_47634,
  title = {{K\$^2\$IE: Kernel Method-based Kernel Intensity Estimators for Inhomogeneous Poisson Processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44963},
}

@inproceedings{icml2026_sika_52562,
  title = {{SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=XClFDlw3oe},
}

@inproceedings{neurips2025_recurrent_75776,
  title = {{Recurrent Memory for Online Interdomain Gaussian Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=BtPg90UEbw},
}

@inproceedings{neurips2025_sparse_76517,
  title = {{Sparse Gaussian Processes: Structured Approximations and Power-EP Revisited}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=KG7j1V0rzS},
}

@inproceedings{iclr2026_diffusion_89543,
  title = {{Diffusion Bridge Variational Inference for Deep Gaussian Processes}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=zyRmy0Ch9a},
}

@inproceedings{neurips2026_predictively_90953,
  title = {{Predictively-Oriented Kalman Filtering}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/156147},
}

@inproceedings{neurips2026_scalable_91713,
  title = {{Scalable Derivative Gaussian Processes via Exact Gradient Reduction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/156058},
}

@inproceedings{icml2024_amortized_44214,
  title = {{Amortized Variational Deep Kernel Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34238},
}

@inproceedings{icml2024_adaptive_44896,
  title = {{Adaptive Observation Cost Control for Variational Quantum Eigensolvers}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33556},
}

@inproceedings{icml2024_sparse_45149,
  title = {{Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33303},
}

@inproceedings{icml2024_energy_45659,
  title = {{Energy-Efficient Gaussian Processes Using Low-Precision Arithmetic}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32793},
}

@inproceedings{icml2025_bayesian_47976,
  title = {{Bayesian Basis Function Approximation for Scalable Gaussian Process Priors in Deep Generative Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44629},
}

@inproceedings{iclr2026_robust_84523,
  title = {{Robust Generalized Schr"odinger Bridge via Sparse Variational Gaussian Processes}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=3a2QuEzveq},
}

@inproceedings{iclr2026_graph_84919,
  title = {{Graph Random Features for Scalable Gaussian Processes}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=89SQfLguNn},
}