% Key papers for Data augmentation and generalization bias (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2022_why_64417,
  title = {{Why do tree-based models still outperform deep learning on typical tabular data?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/55627},
}

@inproceedings{cvpr2019_spottune_14867,
  title = {{SpotTune: Transfer Learning Through Adaptive Fine-Tuning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{aaai2016_transfer_105449,
  title = {{Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/9906},
}

@inproceedings{cvpr2019_learning_14410,
  title = {{Learning Not to Learn: Training Deep Neural Networks With Biased Data}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{aaai2016_unsupervised_104996,
  title = {{Unsupervised Feature Selection with Structured Graph Optimization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10168},
}

@inproceedings{cvpr2021_deep_16832,
  title = {{Deep Stable Learning for Out-of-Distribution Generalization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2020_towards_16357,
  title = {{Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2019_ranked_14701,
  title = {{Ranked List Loss for Deep Metric Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2022_exact_18786,
  title = {{Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{iclr2018_on_28662,
  title = {{On the importance of single directions for generalization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/232},
}

@inproceedings{iclr2020_learning_29693,
  title = {{Learning The Difference That Makes A Difference With Counterfactually-Augmented Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=Sklgs0NFvr},
}

@inproceedings{neurips2019_failing_58253,
  title = {{Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13358},
}

@inproceedings{aaai2020_adaptive_101908,
  title = {{Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5768},
}

@inproceedings{aaai2021_to_1515,
  title = {{To Choose or to Fuse? Scale Selection for Crowd Counting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/02576-to-choose-or-to-fuse-scale-selection-for-crowd-counting/},
}

@inproceedings{cvpr2022_cafe_18407,
  title = {{CAFE: Learning To Condense Dataset by Aligning Features}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{iclr2017_generative_28263,
  title = {{Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=HJWHIKqgl},
}

@inproceedings{aaai2017_identifying_104444,
  title = {{Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10821},
}

@inproceedings{cvpr2021_fair_17003,
  title = {{Fair Attribute Classification Through Latent Space De-Biasing}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2019_discovering_14086,
  title = {{Discovering Fair Representations in the Data Domain}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{neurips2020_do_59733,
  title = {{Do Adversarially Robust ImageNet Models Transfer Better?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18492},
}

@inproceedings{cvpr2024_preserving_24509,
  title = {{Preserving Fairness Generalization in Deepfake Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29203},
}

@inproceedings{aaai2024_m3d_6559,
  title = {{M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28784},
}

@inproceedings{aaai2025_exploring_9083,
  title = {{Exploring Unbiased Deepfake Detection via Token-Level Shuffling and Mixing}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32312},
}

@inproceedings{cvpr2024_fourier_23502,
  title = {{Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31059},
}

@inproceedings{cvpr2025_freqdebias_26321,
  title = {{FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33990},
}

@inproceedings{cvpr2024_exploiting_23408,
  title = {{Exploiting Inter-sample and Inter-feature Relations in Dataset Distillation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30821},
}

@inproceedings{aaai2024_selective_7283,
  title = {{Selective Deep Autoencoder for Unsupervised Feature Selection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29123},
}

@inproceedings{icml2024_position_43878,
  title = {{Position: The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34574},
}

@inproceedings{cvpr2024_navigate_24255,
  title = {{Navigate Beyond Shortcuts: Debiased Learning Through the Lens of Neural Collapse}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31106},
}

@inproceedings{iclr2024_tabr_35385,
  title = {{TabR: Tabular Deep Learning Meets Nearest Neighbors}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17688},
}

@inproceedings{aaai2024_feature_5978,
  title = {{Feature Distribution Matching by Optimal Transport for Effective and Robust Coreset Selection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28771},
}

@inproceedings{iclr2025_a_82200,
  title = {{A Decade's Battle on Dataset Bias: Are We There Yet?}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29591},
}

@inproceedings{aaai2024_contributing_5501,
  title = {{Contributing Dimension Structure of Deep Feature for Coreset Selection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28758},
}

@inproceedings{neurips2024_graphmetro_72120,
  title = {{GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95211},
}

@inproceedings{iclr2024_on_34514,
  title = {{On the Foundations of Shortcut Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18560},
}

@inproceedings{iclr2024_real_35444,
  title = {{Real-Fake: Effective Training Data Synthesis Through Distribution Matching}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17629},
}

@inproceedings{aaai2024_safe_7247,
  title = {{Safe Abductive Learning in the Presence of Inaccurate Rules}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29572},
}

@inproceedings{iclr2025_a_83557,
  title = {{A new framework for evaluating model out-of-distribution generalisation for the biochemical domain}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28260},
}

@inproceedings{aaai2025_metric_9948,
  title = {{Metric-Agnostic Continual Learning for Sustainable Group Fairness}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34052},
}

@inproceedings{aaai2025_partially_10275,
  title = {{Partially Blinded Unlearning: Class Unlearning for Deep Networks from Bayesian Perspective}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32682},
}