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

@inproceedings{aaai2021_on_1100,
  title = {{On the Tractability of SHAP Explanations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/06505-on-the-tractability-of-shap-explanations/},
}

@inproceedings{icml2019_data_37267,
  title = {{Data Shapley: Equitable Valuation of Data for Machine Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4290},
}

@inproceedings{icml2020_problems_38645,
  title = {{Problems with Shapley-value-based explanations as feature importance measures}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6519},
}

@inproceedings{icml2019_explaining_37343,
  title = {{Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3901},
}

@inproceedings{iclr2019_l_28997,
  title = {{L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1065},
}

@inproceedings{aaai2021_if_745,
  title = {{If You Like Shapley Then You’ll Love the Core}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/05751-if-you-like-shapley-then-youll-love-the-core/},
}

@inproceedings{icml2020_the_38849,
  title = {{The Many Shapley Values for Model Explanation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5812},
}

@inproceedings{neurips2021_shapley_61182,
  title = {{Shapley Residuals: Quantifying the limits of the Shapley value for explanations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28660},
}

@inproceedings{aaai2021_the_1504,
  title = {{The Tractability of SHAP-Score-Based Explanations for Classification over Deterministic and Decomposable Boolean Circuits}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/06670-the-tractability-of-shap-score-based-explanations-for-classification-over-deterministic-and-decomposable-boolean-circuits/},
}

@inproceedings{aaai2016_using_105382,
  title = {{Using the Shapley Value to Analyze Algorithm Portfolios}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10440},
}

@inproceedings{icml2020_a_37865,
  title = {{A Distributional Framework For Data Valuation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6637},
}

@inproceedings{icml2023_faith_41465,
  title = {{Faith-Shap: The Faithful Shapley Interaction Index}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/25670},
}

@inproceedings{aaai2024_shaping_7330,
  title = {{Shaping Up SHAP: Enhancing Stability through Layer-Wise Neighbor Selection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29208},
}

@inproceedings{aaai2024_beyond_5289,
  title = {{Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29352},
}

@inproceedings{neurips2023_explaining_66913,
  title = {{Explaining Predictive Uncertainty with Information Theoretic Shapley Values}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72733},
}

@inproceedings{icml2020_the_38855,
  title = {{The Shapley Taylor Interaction Index}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5853},
}

@inproceedings{neurips2024_shapiq_73621,
  title = {{shapiq: Shapley Interactions for Machine Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97533},
}

@inproceedings{iclr2021_shapley_30262,
  title = {{Shapley explainability on the data manifold}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2856},
}

@inproceedings{aaai2023_interventional_3927,
  title = {{Interventional SHAP Values and Interaction Values for Piecewise Linear Regression Trees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26322},
}

@inproceedings{aaai2024_shap_7225,
  title = {{SHAP@k: Efficient and Probably Approximately Correct (PAC) Identification of Top-K Features}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29205},
}

@inproceedings{iclr2024_data_35364,
  title = {{Data Debugging with Shapley Importance over Machine Learning Pipelines}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17709},
}

@inproceedings{neurips2024_du_74271,
  title = {{DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93286},
}

@inproceedings{iclr2025_provably_83994,
  title = {{Provably Accurate Shapley Value Estimation via Leverage Score Sampling}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27831},
}

@inproceedings{neurips2025_shap_80141,
  title = {{SHAP values via sparse Fourier representation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=wab4BEAUt6},
}

@inproceedings{icml2024_kernelshap_44884,
  title = {{KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33568},
}

@inproceedings{icml2024_on_45079,
  title = {{On the Tractability of SHAP Explanations under Markovian Distributions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33373},
}

@inproceedings{icml2024_rethinking_45275,
  title = {{Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33177},
}

@inproceedings{neurips2025_regression_77103,
  title = {{Regression-adjusted Monte Carlo Estimators for Shapley Values and Probabilistic Values}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Qabko39AS5},
}

@inproceedings{neurips2025_shap_78715,
  title = {{SHAP zero Explains Biological Sequence Models with Near-zero Marginal Cost for Future Queries}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=hb2DeWIkmE},
}

@inproceedings{iclr2025_data_81537,
  title = {{Data Shapley in One Training Run}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30239},
}

@inproceedings{aaai2025_support_10875,
  title = {{Support Vector-based Estimation of Multilinear Games for Feature Selection and Explanation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34149},
}

@inproceedings{aaai2025_tab_10921,
  title = {{Tab-Shapley: Identifying Top-k Tabular Data Quality Insights}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33355},
}

@inproceedings{aaai2025_unlocking_11171,
  title = {{Unlocking the Game: Estimating Games in Möbius Representation for Explanation and High-Order Interaction Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34148},
}

@inproceedings{icml2025_fast_46887,
  title = {{Fast Estimation of Partial Dependence Functions using Trees}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45692},
}

@inproceedings{icml2025_prediction_47729,
  title = {{Prediction via Shapley Value Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44871},
}

@inproceedings{icml2026_verified_49613,
  title = {{Verified SHAP: Provable Bounds for Exact Shapley Values of Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=48c42LVtLx},
}