% Key papers for Compact interpretable model classes (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2021_neural_63472,
  title = {{Neural Additive Models: Interpretable Machine Learning with Neural Nets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28229},
}

@inproceedings{cvpr2016_the_11920,
  title = {{The Global Patch Collider}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{aaai2020_addmc_100637,
  title = {{ADDMC: Weighted Model Counting with Algebraic Decision Diagrams}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5505},
}

@inproceedings{neurips2016_fast_55727,
  title = {{Fast and Flexible Monotonic Functions with Ensembles of Lattices}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7302},
}

@inproceedings{aaai2016_zero_104960,
  title = {{Zero-Suppressed Sentential Decision Diagrams}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10114},
}

@inproceedings{aaai2016_bagging_105458,
  title = {{Bagging Ensembles for the Diagnosis and Prognostication of Alzheimer's Disease}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/9915},
}

@inproceedings{aaai2016_optimizing_105454,
  title = {{Optimizing Resilience in Large Scale Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/9911},
}

@inproceedings{aaai2019_deep_101222,
  title = {{Deep Neural Networks Constrained by Decision Rules}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4095},
}

@inproceedings{aaai2022_optimizing_2523,
  title = {{Optimizing Binary Decision Diagrams with MaxSAT for Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/03767-optimizing-binary-decision-diagrams-with-maxsat-for-classification/},
}

@inproceedings{neurips2022_on_66495,
  title = {{On Computing Probabilistic Explanations for Decision Trees}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52966},
}

@inproceedings{aaai2021_cascade_235,
  title = {{Cascade Size Distributions: Why They Matter and How to Compute Them Efficiently}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/06840-cascade-size-distributions-why-they-matter-and-how-to-compute-them-efficiently/},
}

@inproceedings{aaai2021_submodel_1442,
  title = {{Submodel Decomposition Bounds for Influence Diagrams}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/12147-submodel-decomposition-bounds-for-influence-diagrams/},
}

@inproceedings{aaai2021_robust_1292,
  title = {{Robust Model Compression Using Deep Hypotheses}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/06688-robust-model-compression-using-deep-hypotheses/},
}

@inproceedings{neurips2023_mlfmf_67753,
  title = {{MLFMF: Data Sets for Machine Learning for Mathematical Formalization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/73626},
}

@inproceedings{aaai2021_counterfactual_334,
  title = {{Counterfactual Explanations for Oblique Decision Trees:Exact, Efficient Algorithms}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/06903-counterfactual-explanations-for-oblique-decision-treesexact-efficient-algorithms/},
}

@inproceedings{aaai2023_evaluating_3595,
  title = {{Evaluating Factors Influencing COVID-19 Outcomes across Countries Using Decision Trees (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/27011},
}

@inproceedings{neurips2024_using_72890,
  title = {{Using Noise to Infer Aspects of Simplicity Without Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94517},
}

@inproceedings{iclr2021_one_30716,
  title = {{One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3253},
}

@inproceedings{icml2022_discrete_40460,
  title = {{Discrete Tree Flows via Tree-Structured Permutations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16691},
}

@inproceedings{icml2023_learning_43173,
  title = {{Learning Prescriptive ReLU Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/23936},
}

@inproceedings{icml2025_what_48363,
  title = {{What makes an Ensemble (Un) Interpretable?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44253},
}

@inproceedings{icml2025_prediction_48641,
  title = {{Prediction models that learn to avoid missing values}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43980},
}

@inproceedings{neurips2025_from_75329,
  title = {{From Counterfactuals to Trees: Competitive Analysis of Model Extraction Attacks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=6vcgsrK6pN},
}

@inproceedings{neurips2026_counterfactual_93870,
  title = {{Counterfactual Maps: What They Are and How to Find Them}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/153015},
}

@inproceedings{aaai2024_a_5027,
  title = {{A General Theoretical Framework for Learning Smallest Interpretable Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28937},
}