% Key papers for Provable post-hoc explanations (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2017_axiomatic_36077,
  title = {{Axiomatic Attribution for Deep Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/850},
}

@inproceedings{aaai2018_anchors_101248,
  title = {{Anchors: High-Precision Model-Agnostic Explanations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11491},
}

@inproceedings{icml2017_understanding_36439,
  title = {{Understanding Black-box Predictions via Influence Functions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/678},
}

@inproceedings{neurips2019_explanations_58235,
  title = {{Explanations can be manipulated and geometry is to blame}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14293},
}

@inproceedings{iclr2021_learning_30540,
  title = {{Learning explanations that are hard to vary}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2721},
}

@inproceedings{neurips2020_model_60333,
  title = {{Model Interpretability through the lens of Computational Complexity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18414},
}

@inproceedings{cvpr2022_towards_20036,
  title = {{Towards Better Understanding Attribution Methods}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{icml2020_fairwashing_38195,
  title = {{Fairwashing explanations with off-manifold detergent}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6394},
}

@inproceedings{iclr2020_towards_29352,
  title = {{Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=BkxRRkSKwr},
}

@inproceedings{aaai2021_right_1285,
  title = {{Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/09533-right-for-better-reasons-training-differentiable-models-by-constraining-their-influence-functions/},
}

@inproceedings{cvpr2023_shortcomings_22199,
  title = {{Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21091},
}

@inproceedings{neurips2020_model_60330,
  title = {{Model Agnostic Multilevel Explanations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17156},
}

@inproceedings{icml2022_a_40171,
  title = {{A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17261},
}

@inproceedings{aaai2021_a_61,
  title = {{A Unified Taylor Framework for Revisiting Attribution Methods}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/11462-a-unified-taylor-framework-for-revisiting-attribution-methods/},
}

@inproceedings{neurips2021_synthetic_62204,
  title = {{Synthetic Benchmarks for Scientific Research in Explainable Machine Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://openreview.net/forum?id=R7vr14ffhF9},
}

@inproceedings{aaai2024_integrated_6332,
  title = {{Integrated Decision Gradients: Compute Your Attributions Where the Model Makes Its Decision}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28336},
}

@inproceedings{neurips2017_streaming_56696,
  title = {{Streaming Weak Submodularity: Interpreting Neural Networks on the Fly}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9185},
}

@inproceedings{icml2022_framework_40585,
  title = {{Framework for Evaluating Faithfulness of Local Explanations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17445},
}

@inproceedings{neurips2020_learning_60216,
  title = {{Learning outside the Black-Box: The pursuit of interpretable models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17215},
}

@inproceedings{aaai2022_constraint_1867,
  title = {{Constraint-Driven Explanations for Black-Box ML Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08304-constraint-driven-explanations-for-black-box-ml-models/},
}

@inproceedings{aaai2024_mfaba_6574,
  title = {{MFABA: A More Faithful and Accelerated Boundary-Based Attribution Method for Deep Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29669},
}

@inproceedings{aaai2024_accelerating_5114,
  title = {{Accelerating the Global Aggregation of Local Explanations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29845},
}

@inproceedings{aaai2024_g_6169,
  title = {{G–LIME: Statistical Learning for Local Interpretations of Deep Neural Networks Using Global Priors (Abstract Reprint)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30605},
}

@inproceedings{icml2024_local_45676,
  title = {{Local vs. Global Interpretability: A Computational Complexity Perspective}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32776},
}

@inproceedings{aaai2024_learning_6473,
  title = {{Learning Performance Maximizing Ensembles with Explainability Guarantees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29378},
}

@inproceedings{neurips2024_auditing_74582,
  title = {{Auditing Local Explanations is Hard}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93005},
}

@inproceedings{iclr2026_nimo_86287,
  title = {{NIMO: a Nonlinear Interpretable MOdel}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=OHn8jDdpFk},
}

@inproceedings{aaai2026_learning_106660,
  title = {{Learning from Answer Sets via Single-Shot Disjunctive ASP Encoding}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/38967},
}

@inproceedings{cvpr2024_identifying_23751,
  title = {{Identifying Important Group of Pixels using Interactions}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29751},
}

@inproceedings{icml2024_explaining_43372,
  title = {{Explaining Probabilistic Models with Distributional Values}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35080},
}

@inproceedings{icml2024_trust_43903,
  title = {{Trust Regions for Explanations via Black-Box Probabilistic Certification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34549},
}

@inproceedings{icml2024_saliency_44938,
  title = {{Saliency strikes back: How filtering out high frequencies improves white-box explanations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33514},
}

@inproceedings{icml2025_explaining_48514,
  title = {{Explaining, Fast and Slow: Abstraction and Refinement of Provable Explanations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44106},
}

@inproceedings{icml2026_beyond_51641,
  title = {{Beyond Additive Decompositions: Interpretability Through Separability}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=OMk6KBe0WR},
}

@inproceedings{icml2026_explanations_53437,
  title = {{Explanations are a Means to an End: Decision Theoretic Explanation Evaluation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=fVso2kGVrF},
}

@inproceedings{neurips2024_improving_71362,
  title = {{Improving Decision Sparsity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95887},
}

@inproceedings{neurips2025_correcting_74884,
  title = {{Correcting misinterpretations of additive models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=2ClM0g9OFT},
}

@inproceedings{neurips2025_additive_74917,
  title = {{Additive Models Explained: A Computational Complexity Approach}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=2YAGKZVXQC},
}

@inproceedings{neurips2025_smoothed_75391,
  title = {{Smoothed Differentiation Efficiently Mitigates Shattered Gradients in Explanations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=7YKbyBb0kF},
}