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

@inproceedings{neurips2017_learning_56442,
  title = {{Learning to Model the Tail}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9469},
}

@inproceedings{neurips2016_learning_55838,
  title = {{Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://papers.nips.cc/paper_files/paper/2016/hash/140f6969d5213fd0ece03148e62e461e-Abstract.html},
}

@inproceedings{cvpr2023_diversity_20828,
  title = {{Diversity-Aware Meta Visual Prompting}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/23049},
}

@inproceedings{iclr2019_learning_28766,
  title = {{Learning to Learn with Conditional Class Dependencies}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1038},
}

@inproceedings{cvpr2021_metahtr_17405,
  title = {{MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{neurips2020_mesa_60278,
  title = {{MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17343},
}

@inproceedings{icml2020_tasknorm_38831,
  title = {{TaskNorm: Rethinking Batch Normalization for Meta-Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6200},
}

@inproceedings{icml2020_metafun_38451,
  title = {{MetaFun: Meta-Learning with Iterative Functional Updates}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6581},
}

@inproceedings{icml2021_data_39194,
  title = {{Data Augmentation for Meta-Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10493},
}

@inproceedings{aaai2020_neural_103117,
  title = {{Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6427},
}

@inproceedings{iclr2020_meta_29295,
  title = {{Meta Dropout: Learning to Perturb Latent Features for Generalization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=BJgd81SYwr},
}

@inproceedings{iclr2022_meta_31175,
  title = {{Meta Discovery: Learning to Discover Novel Classes given Very Limited Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6442},
}

@inproceedings{aaai2022_learngene_2310,
  title = {{Learngene: From Open-World to Your Learning Task}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08557-learngene-from-open-world-to-your-learning-task/},
}

@inproceedings{neurips2019_meta_58590,
  title = {{Meta-Curvature}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13509},
}

@inproceedings{neurips2019_meta_58595,
  title = {{Meta-Surrogate Benchmarking for Hyperparameter Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13741},
}

@inproceedings{aaai2023_cmvae_3221,
  title = {{CMVAE: Causal Meta VAE for Unsupervised Meta-Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26135},
}

@inproceedings{iclr2021_meta_30737,
  title = {{Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3317},
}

@inproceedings{icml2023_towards_42222,
  title = {{Towards Omni-generalizable Neural Methods for Vehicle Routing Problems}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/25267},
}

@inproceedings{aaai2023_meta_4145,
  title = {{Meta-Sketch: A Neural Data Structure for Estimating Item Frequencies of Data Streams}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25846},
}

@inproceedings{cvpr2023_a_20291,
  title = {{A Meta-Learning Approach to Predicting Performance and Data Requirements}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21020},
}

@inproceedings{cvpr2024_free_23434,
  title = {{FREE: Faster and Better Data-Free Meta-Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30619},
}

@inproceedings{aaai2024_referee_7086,
  title = {{Referee-Meta-Learning for Fast Adaptation of Locational Fairness}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30197},
}

@inproceedings{cvpr2024_improving_23771,
  title = {{Improving Generalization via Meta-Learning on Hard Samples}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31273},
}

@inproceedings{iclr2024_quick_35474,
  title = {{Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17599},
}

@inproceedings{neurips2025_datarater_80041,
  title = {{DataRater: Meta-Learned Dataset Curation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=vUtQFnlDyv},
}

@inproceedings{aaai2025_consistency_8505,
  title = {{Consistency of Compositional Generalization Across Multiple Levels}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32492},
}

@inproceedings{neurips2024_mixture_71459,
  title = {{Mixture of Adversarial LoRAs: Boosting Robust Generalization in Meta-Tuning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95797},
}

@inproceedings{iclr2026_liteguard_86733,
  title = {{LiteGuard: Efficient Task-Agnostic Model Fingerprinting with Enhanced Generalization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=TFC25ZT9nI},
}

@inproceedings{iclr2026_mu_87752,
  title = {{\$mu\$LO: Compute-Efficient Meta-Generalization of Learned Optimizers}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=f8z2bzOLK2},
}