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

@inproceedings{cvpr2016_raid_11782,
  title = {{RAID-G: Robust Estimation of Approximate Infinite Dimensional Gaussian With Application to Material Recognition}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{neurips2018_heterogeneous_57199,
  title = {{Heterogeneous Multi-output Gaussian Process Prediction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11648},
}

@inproceedings{icml2021_classifying_39136,
  title = {{Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8535},
}

@inproceedings{aaai2016_near_105037,
  title = {{Near-Optimal Active Learning of Multi-Output Gaussian Processes}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10209},
}

@inproceedings{icml2018_generalized_36713,
  title = {{Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1958},
}

@inproceedings{aaai2021_conceptualized_290,
  title = {{Conceptualized and Contextualized Gaussian Embedding}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/13683-conceptualized-and-contextualized-gaussian-embedding/},
}

@inproceedings{neurips2017_efficient_56283,
  title = {{Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9289},
}

@inproceedings{neurips2019_multi_58636,
  title = {{Multi-resolution Multi-task Gaussian Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14331},
}

@inproceedings{neurips2018_variational_57762,
  title = {{Variational Learning on Aggregate Outputs with Gaussian Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11590},
}

@inproceedings{neurips2019_spatially_59003,
  title = {{Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13483},
}

@inproceedings{icml2017_scalable_36383,
  title = {{Scalable Multi-Class Gaussian Process Classification using Expectation Propagation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/506},
}

@inproceedings{neurips2019_statistical_59017,
  title = {{Statistical Model Aggregation via Parameter Matching}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14094},
}

@inproceedings{aaai2016_dintucker_105050,
  title = {{DinTucker: Scaling Up Gaussian Process Models on Large Multidimensional Arrays}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10222},
}

@inproceedings{icml2016_nonlinear_35921,
  title = {{Nonlinear Statistical Learning with Truncated Gaussian Graphical Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/su16.html},
}

@inproceedings{iclr2021_bayesian_30601,
  title = {{Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3334},
}

@inproceedings{neurips2019_multi_58638,
  title = {{Multi-task Learning for Aggregated Data using Gaussian Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14413},
}

@inproceedings{neurips2021_a_61302,
  title = {{A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27625},
}

@inproceedings{icml2019_bayesian_37179,
  title = {{Bayesian Deconditional Kernel Mean Embeddings}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3983},
}

@inproceedings{icml2020_deep_38086,
  title = {{Deep Gaussian Markov Random Fields}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6564},
}

@inproceedings{icml2020_non_38515,
  title = {{Non-separable Non-stationary random fields}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6398},
}

@inproceedings{iclr2024_functional_34681,
  title = {{Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18393},
}

@inproceedings{icml2026_transformed_50458,
  title = {{Transformed Latent Variable Multi-Output Gaussian Processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=CXKhhN5j1N},
}

@inproceedings{icml2026_belief_50774,
  title = {{Belief Propagation Converges to Gaussian Distributions in Sparsely-Connected Factor Graphs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=FaGn04tvzq},
}

@inproceedings{neurips2024_idiographic_72347,
  title = {{Idiographic Personality Gaussian Process for Psychological Assessment}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95001},
}

@inproceedings{neurips2025_squared_79908,
  title = {{Squared families are useful conjugate priors}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=uAegI6Hh6G},
}

@inproceedings{icml2024_learning_44053,
  title = {{Learning Mixtures of Gaussian Processes through Random Projection}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34399},
}

@inproceedings{iclr2026_meta_84870,
  title = {{Meta-Learning Theory-Informed Inductive Biases using Deep Kernel Gaussian Processes}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=7dvYWzOiEu},
}