% Key papers for Bayesian view of meta-learning (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2017_deep_36130,
  title = {{Deep Bayesian Active Learning with Image Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/794},
}

@inproceedings{neurips2018_bayesian_56900,
  title = {{Bayesian Model-Agnostic Meta-Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11706},
}

@inproceedings{iclr2018_recasting_28433,
  title = {{Recasting Gradient-Based Meta-Learning as Hierarchical Bayes}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/313},
}

@inproceedings{iclr2019_amortized_29185,
  title = {{Amortized Bayesian Meta-Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/940},
}

@inproceedings{neurips2018_bayesian_56896,
  title = {{Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11780},
}

@inproceedings{neurips2017_robust_56637,
  title = {{Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9396},
}

@inproceedings{neurips2018_bayesian_56898,
  title = {{Bayesian Inference of Temporal Task Specifications from Demonstrations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11379},
}

@inproceedings{aaai2020_variational_101892,
  title = {{Variational Metric Scaling for Metric-Based Meta-Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5752},
}

@inproceedings{icml2021_pacoh_39762,
  title = {{PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10421},
}

@inproceedings{neurips2019_the_59061,
  title = {{The Functional Neural Process}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13928},
}

@inproceedings{aaai2023_beta_3154,
  title = {{BETA-CD: A Bayesian Meta-Learned Cognitive Diagnosis Framework for Personalized Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25629},
}

@inproceedings{iclr2020_learning_29820,
  title = {{Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=rkeZIJBYvr},
}

@inproceedings{neurips2019_hyperparameter_58382,
  title = {{Hyperparameter Learning via Distributional Transfer}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13785},
}

@inproceedings{neurips2019_mutually_58650,
  title = {{Mutually Regressive Point Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13654},
}

@inproceedings{aaai2023_scalable_4609,
  title = {{Scalable Bayesian Meta-Learning through Generalized Implicit Gradients}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26337},
}

@inproceedings{neurips2019_learning_58484,
  title = {{Learning Hawkes Processes from a handful of events}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14222},
}

@inproceedings{neurips2023_pretraining_67258,
  title = {{Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72459},
}

@inproceedings{neurips2021_variational_61125,
  title = {{Variational Multi-Task Learning with Gumbel-Softmax Priors}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27710},
}

@inproceedings{iclr2020_bayesian_29359,
  title = {{Bayesian Meta Sampling for Fast Uncertainty Adaptation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=Bkxv90EKPB},
}

@inproceedings{aaai2019_orthogonality_101653,
  title = {{Orthogonality-Promoting Dictionary Learning via Bayesian Inference}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4360},
}

@inproceedings{iclr2024_recombiner_34579,
  title = {{RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18495},
}

@inproceedings{neurips2024_divide_73123,
  title = {{Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94307},
}

@inproceedings{iclr2025_inverse_84187,
  title = {{Inverse decision-making using neural amortized Bayesian actors}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27641},
}

@inproceedings{aaai2025_hypernetwork_9480,
  title = {{Hypernetwork Approach to Bayesian MAML (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35239},
}

@inproceedings{iclr2024_in_34083,
  title = {{In-Context Learning through the Bayesian Prism}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18992},
}

@inproceedings{iclr2024_a_35179,
  title = {{A Hierarchical Bayesian Model for Few-Shot Meta Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17894},
}

@inproceedings{icml2024_bayesian_43961,
  title = {{Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34491},
}

@inproceedings{icml2025_can_46321,
  title = {{Can Transformers Learn Full Bayesian Inference in Context?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46240},
}

@inproceedings{icml2026_ballast_49276,
  title = {{BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=0xOj6kVMbb},
}

@inproceedings{icml2026_sparse_49525,
  title = {{Sparse Bayesian Deep Functional Learning with Structured Region Selection}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=3IFIedDIoN},
}

@inproceedings{icml2026_incremental_53251,
  title = {{Incremental Transformer Neural Processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=diRgjfD2vu},
}

@inproceedings{neurips2025_learning_75042,
  title = {{Learning to Generalize: An Information Perspective on Neural Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=3iEWfZh5R2},
}

@inproceedings{neurips2025_in_78123,
  title = {{In-Context Learning Strategies Emerge Rationally}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=bBUUOQI0N6},
}

@inproceedings{iclr2025_can_82697,
  title = {{Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29101},
}

@inproceedings{iclr2026_amortising_85957,
  title = {{Amortising Inference and Meta-Learning Priors in Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=KG6SSTz2GJ},
}

@inproceedings{neurips2026_amortized_93383,
  title = {{Amortized Bayesian Experimental Design with In-Context Knowledge Conditioning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/152127},
}

@inproceedings{cvpr2026_rebapl_98811,
  title = {{ReBaPL: Repulsive Bayesian Prompt Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Bendou_ReBaPL_Repulsive_Bayesian_Prompt_Learning_CVPR_2026_paper.html},
}

@inproceedings{icml2026_in_50347,
  title = {{In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=BUFSSOuphA},
}

@inproceedings{icml2026_fine_53751,
  title = {{Fine-Tune Once, Reuse Across Models: Bayesian Task-Update Factors and Approximations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=iLS4oNpEQ1},
}

@inproceedings{neurips2025_variational_80384,
  title = {{Variational Task Vector Composition}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=yzv6kysYbw},
}