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

@inproceedings{iclr2017_optimization_28364,
  title = {{Optimization as a Model for Few-Shot Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=rJY0-Kcll},
}

@inproceedings{neurips2016_learning_55844,
  title = {{Learning to learn by gradient descent by gradient descent}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7042},
}

@inproceedings{iclr2020_meta_29823,
  title = {{Meta-Learning with Warped Gradient Descent}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=rkeiQlBFPB},
}

@inproceedings{iclr2018_meta_28567,
  title = {{Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/185},
}

@inproceedings{icml2018_gradient_36725,
  title = {{Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2202},
}

@inproceedings{aaai2024_metadiff_6633,
  title = {{MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29608},
}

@inproceedings{neurips2019_efficient_58191,
  title = {{Efficient Meta Learning via Minibatch Proximal Update}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14494},
}

@inproceedings{icml2019_provable_37670,
  title = {{Provable Guarantees for Gradient-Based Meta-Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3887},
}

@inproceedings{iclr2018_understanding_28484,
  title = {{Understanding Short-Horizon Bias in Stochastic Meta-Optimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/240},
}

@inproceedings{cvpr2023_meta_21555,
  title = {{Meta-Learning With a Geometry-Adaptive Preconditioner}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21191},
}

@inproceedings{neurips2020_convergence_59599,
  title = {{Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17966},
}

@inproceedings{neurips2018_probabilistic_57531,
  title = {{Probabilistic Model-Agnostic Meta-Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11905},
}

@inproceedings{neurips2019_adaptive_57870,
  title = {{Adaptive Gradient-Based Meta-Learning Methods}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13712},
}

@inproceedings{neurips2019_learning_58531,
  title = {{Learning to Optimize in Swarms}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14410},
}

@inproceedings{icml2022_sharp_41170,
  title = {{Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16661},
}

@inproceedings{neurips2021_multi_63476,
  title = {{Multi-Objective Meta Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27889},
}

@inproceedings{cvpr2025_towards_27895,
  title = {{Towards Consistent Multi-Task Learning: Unlocking the Potential of Task-Specific Parameters}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34415},
}

@inproceedings{iclr2022_continuous_30863,
  title = {{Continuous-Time Meta-Learning with Forward Mode Differentiation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6055},
}

@inproceedings{icml2021_guarantees_39411,
  title = {{Guarantees for Tuning the Step Size using a Learning-to-Learn Approach}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9003},
}

@inproceedings{neurips2023_meta_68805,
  title = {{Meta-AdaM: An Meta-Learned Adaptive Optimizer with Momentum for Few-Shot Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71046},
}

@inproceedings{aaai2025_task_10929,
  title = {{Task-Specific Preconditioner for Cross-Domain Few-Shot Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33953},
}

@inproceedings{aaai2025_memory_9938,
  title = {{Memory-Reduced Meta-Learning with Guaranteed Convergence}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35501},
}

@inproceedings{neurips2024_in_73328,
  title = {{In-Context Learning State Vector with Inner and Momentum Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94117},
}

@inproceedings{icml2025_metaoptimize_46779,
  title = {{MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45796},
}

@inproceedings{icml2026_the_52549,
  title = {{The Role of Target Update Frequencies in Q-Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=X6i0PB7aJQ},
}

@inproceedings{neurips2025_on_79044,
  title = {{On the Stability and Generalization of Meta-Learning: the Impact of Inner-Levels}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=l1L0Yhh6x6},
}

@inproceedings{iclr2026_binomial_88405,
  title = {{Binomial Gradient-Based Meta-Learning for Enhanced Meta-Gradient Estimation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=mKgUAO41zf},
}

@inproceedings{neurips2026_metake_93020,
  title = {{MetaKE: Meta-Learning for Knowledge Editing Toward a Better Accuracy-Editability Trade-off}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/149932},
}

@inproceedings{iclr2024_meta_34727,
  title = {{Meta-Learning Priors Using Unrolled Proximal Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18347},
}

@inproceedings{iclr2026_towards_88002,
  title = {{Towards Learned Optimization Free Lunch}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=hxDB30LwVe},
}