% Key papers for Feature attribution methods (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2020_understanding_61037,
  title = {{Understanding Global Feature Contributions With Additive Importance Measures}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/19000},
}

@inproceedings{aaai2020_sanity_102202,
  title = {{Sanity Checks for Saliency Metrics}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6064},
}

@inproceedings{aaai2022_do_1996,
  title = {{Do Feature Attribution Methods Correctly Attribute Features?}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/09623-do-feature-attribution-methods-correctly-attribute-features/},
}

@inproceedings{cvpr2023_craft_20554,
  title = {{CRAFT: Concept Recursive Activation FacTorization for Explainability}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22071},
}

@inproceedings{cvpr2020_scout_16153,
  title = {{SCOUT: Self-Aware Discriminant Counterfactual Explanations}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{neurips2020_how_60022,
  title = {{How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17142},
}

@inproceedings{aaai2019_granger_101697,
  title = {{Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4412},
}

@inproceedings{icml2022_a_40142,
  title = {{A Consistent and Efficient Evaluation Strategy for Attribution Methods}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17561},
}

@inproceedings{neurips2021_the_62097,
  title = {{The effectiveness of feature attribution methods and its correlation with automatic evaluation scores}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28492},
}

@inproceedings{neurips2019_disentangling_58150,
  title = {{Disentangling Influence: Using disentangled representations to audit model predictions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13603},
}

@inproceedings{neurips2022_weightedshap_64846,
  title = {{WeightedSHAP: analyzing and improving Shapley based feature attributions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54027},
}

@inproceedings{iclr2021_saliency_30464,
  title = {{Saliency is a Possible Red Herring When Diagnosing Poor Generalization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3280},
}

@inproceedings{aaai2021_accurate_73,
  title = {{Accurate and Robust Feature Importance Estimation under Distribution Shifts}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/07891-accurate-and-robust-feature-importance-estimation-under-distribution-shifts/},
}

@inproceedings{icml2021_marginal_39591,
  title = {{Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10695},
}

@inproceedings{neurips2020_icam_60034,
  title = {{ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17093},
}

@inproceedings{aaai2023_local_4084,
  title = {{Local Path Integration for Attribution}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25422},
}

@inproceedings{icml2019_discovering_37294,
  title = {{Discovering Conditionally Salient Features with Statistical Guarantees}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4238},
}

@inproceedings{aaai2019_understanding_101615,
  title = {{Understanding Learned Models by Identifying Important Features at the Right Resolution}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4318},
}

@inproceedings{neurips2020_learning_60159,
  title = {{Learning Deep Attribution Priors Based On Prior Knowledge}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17496},
}

@inproceedings{neurips2022_what_64513,
  title = {{What You See is What You Classify: Black Box Attributions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54821},
}

@inproceedings{aaai2026_shapbpt_107347,
  title = {{ShapBPT: Image Feature Attributions Using Data-Aware Binary Partition Trees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39699},
}

@inproceedings{iclr2024_less_35046,
  title = {{Less is More: Fewer Interpretable Region via Submodular Subset Selection}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18028},
}

@inproceedings{icml2024_feature_44054,
  title = {{Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34398},
}

@inproceedings{neurips2024_benchmarking_72635,
  title = {{Benchmarking the Attribution Quality of Vision Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97644},
}

@inproceedings{neurips2024_interpretable_74173,
  title = {{Interpretable Generalized Additive Models for Datasets with Missing Values}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93372},
}

@inproceedings{icml2024_provably_43391,
  title = {{Provably Better Explanations with Optimized Aggregation of Feature Attributions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35061},
}

@inproceedings{icml2024_local_44229,
  title = {{Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34223},
}

@inproceedings{icml2024_prospector_44339,
  title = {{Prospector Heads: Generalized Feature Attribution for Large Models \& Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34113},
}

@inproceedings{icml2024_on_44670,
  title = {{On Gradient-like Explanation under a Black-box Setting: When Black-box Explanations Become as Good as White-box}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33782},
}

@inproceedings{icml2025_one_48521,
  title = {{One Wave To Explain Them All: A Unifying Perspective On Feature Attribution}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44099},
}

@inproceedings{neurips2024_data_73529,
  title = {{Data-faithful Feature Attribution: Mitigating Unobservable Confounders via Instrumental Variables}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93948},
}

@inproceedings{neurips2025_regional_79956,
  title = {{Regional Explanations: Bridging Local and Global Variable Importance}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=ubrecCeZrc},
}

@inproceedings{iclr2026_missingness_85000,
  title = {{Missingness Bias Calibration in Feature Attribution Explanations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=9AbJO130G8},
}

@inproceedings{cvpr2026_h_98127,
  title = {{H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Mehrotra_H-Sets_Hessian-Guided_Discovery_of_Set-Level_Feature_Interactions_in_Image_Classifiers_CVPR_2026_paper.html},
}

@inproceedings{aaai2026_distribution_107138,
  title = {{Distribution-Based Feature Attribution for Explaining the Predictions of Any Classifier}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39490},
}

@inproceedings{icml2024_benchmarking_44438,
  title = {{Benchmarking Deletion Metrics with the Principled Explanations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34014},
}

@inproceedings{icml2025_feature_46852,
  title = {{Feature Importance Metrics in the Presence of Missing Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45724},
}

@inproceedings{icml2026_hidden_50541,
  title = {{Hidden in Plain Sight -- Class Competition Focuses Attribution Maps}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=DSZ36ji3Y6},
}

@inproceedings{iclr2026_flow_85566,
  title = {{Flow-Disentangled Feature Importance}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=Fx8AtzGlTS},
}

@inproceedings{neurips2026_in_95011,
  title = {{In search of a definition of importance: Do attributions capture it?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/151550},
}