% Key papers for PAC-Bayes and decoupled GP learning (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2016_deep_35789,
  title = {{Deep Gaussian Processes for Regression using Approximate Expectation Propagation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/bui16.html},
}

@inproceedings{neurips2018_inference_57237,
  title = {{Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11722},
}

@inproceedings{neurips2017_spectral_56679,
  title = {{Spectral Mixture Kernels for Multi-Output Gaussian Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9436},
}

@inproceedings{aaai2021_practical_1181,
  title = {{Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/07439-practical-and-rigorous-uncertainty-bounds-for-gaussian-process-regression/},
}

@inproceedings{neurips2018_scaling_57607,
  title = {{Scaling Gaussian Process Regression with Derivatives}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11662},
}

@inproceedings{aaai2019_robustness_103279,
  title = {{Robustness Guarantees for Bayesian Inference with Gaussian Processes}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4772},
}

@inproceedings{neurips2016_gaussian_55746,
  title = {{Gaussian Processes for Survival Analysis}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7051},
}

@inproceedings{neurips2017_linearly_56454,
  title = {{Linearly constrained Gaussian processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8914},
}

@inproceedings{neurips2019_exact_58229,
  title = {{Exact Gaussian Processes on a Million Data Points}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14378},
}

@inproceedings{cvpr2017_soft_12647,
  title = {{Soft-Margin Mixture of Regressions}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{icml2017_prediction_36344,
  title = {{Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/786},
}

@inproceedings{neurips2020_mat_60294,
  title = {{Matérn Gaussian Processes on Riemannian Manifolds}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17250},
}

@inproceedings{neurips2016_incremental_55788,
  title = {{Incremental Variational Sparse Gaussian Process Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7045},
}

@inproceedings{neurips2018_learning_57283,
  title = {{Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11336},
}

@inproceedings{neurips2018_orthogonally_57488,
  title = {{Orthogonally Decoupled Variational Gaussian Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11832},
}

@inproceedings{icml2017_asynchronous_36071,
  title = {{Asynchronous Distributed Variational Gaussian Process for Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/629},
}

@inproceedings{aaai2018_fourier_102429,
  title = {{Fourier Feature Approximations for Periodic Kernels in Time-Series Modelling}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11696},
}

@inproceedings{icml2019_deep_37275,
  title = {{Deep Gaussian Processes with Importance-Weighted Variational Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4027},
}

@inproceedings{icml2020_randomly_38675,
  title = {{Randomly Projected Additive Gaussian Processes for Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6466},
}

@inproceedings{icml2020_scalable_38732,
  title = {{Scalable Exact Inference in Multi-Output Gaussian Processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6430},
}

@inproceedings{aaai2024_efficient_5811,
  title = {{Efficient Nonparametric Tensor Decomposition for Binary and Count Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29456},
}

@inproceedings{neurips2024_robust_70505,
  title = {{Robust Gaussian Processes via Relevance Pursuit}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96603},
}

@inproceedings{aaai2025_asynchronous_8161,
  title = {{Asynchronous Distributed Gaussian Process Regression}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34359},
}

@inproceedings{icml2024_self_43430,
  title = {{Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35022},
}

@inproceedings{icml2024_robust_43478,
  title = {{Robust and Conjugate Gaussian Process Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34974},
}

@inproceedings{aaai2026_streaming_107641,
  title = {{Streaming Generated Gaussian Process Experts for Online Learning and Control}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39993},
}

@inproceedings{aaai2025_learning_9759,
  title = {{Learning from Summarized Data: Gaussian Process Regression with Sample Quasi-Likelihood}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34252},
}

@inproceedings{icml2024_stationarity_43516,
  title = {{Stationarity without mean reversion in improper Gaussian processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34936},
}

@inproceedings{icml2025_scalable_47112,
  title = {{Scalable Gaussian Processes with Latent Kronecker Structure}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45475},
}

@inproceedings{icml2025_robust_47677,
  title = {{Robust and Conjugate Spatio-Temporal Gaussian Processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44920},
}

@inproceedings{icml2026_empirical_51675,
  title = {{Empirical Gaussian Processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Oj7ZwBhiyE},
}

@inproceedings{neurips2025_gaussian_75758,
  title = {{Gaussian Processes for Shuffled Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=BlcTbgxr4Z},
}

@inproceedings{neurips2025_turbocharging_76190,
  title = {{Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=GaL0ja9ygG},
}

@inproceedings{neurips2025_staci_79620,
  title = {{STACI: Spatio-Temporal Aleatoric Conformal Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=rHMjAiaLzi},
}

@inproceedings{neurips2025_robust_79825,
  title = {{Robust and Computation-Aware Gaussian Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=tJZKaDSSTX},
}

@inproceedings{iclr2025_identifiability_81435,
  title = {{Identifiability for Gaussian Processes with Holomorphic Kernels}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30338},
}

@inproceedings{iclr2025_deep_81948,
  title = {{Deep Random Features for Scalable Interpolation of Spatiotemporal Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29836},
}

@inproceedings{neurips2026_conditioning_93600,
  title = {{Conditioning Gaussian Processes on Almost Anything}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/155789},
}

@inproceedings{aaai2026_practical_107198,
  title = {{Practical Global and Local Bounds in Gaussian Process Regression via Chaining}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39550},
}

@inproceedings{aaai2026_exact_107271,
  title = {{Exact Shapley Attributions in Quadratic-time for FANOVA Gaussian Processes}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39623},
}