% Key papers for Optimal decision tree learning (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2020_learning_101851,
  title = {{Learning Optimal Decision Trees Using Caching Branch-and-Bound Search}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5711},
}

@inproceedings{neurips2016_a_55528,
  title = {{A Communication-Efficient Parallel Algorithm for Decision Tree}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7252},
}

@inproceedings{neurips2017_variable_56762,
  title = {{Variable Importance Using Decision Trees}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8839},
}

@inproceedings{aaai2019_compiling_103304,
  title = {{Compiling Bayesian Network Classifiers into Decision Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4797},
}

@inproceedings{aaai2020_efficient_101857,
  title = {{Efficient Inference of Optimal Decision Trees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5717},
}

@inproceedings{neurips2018_alternating_56860,
  title = {{Alternating optimization of decision trees, with application to learning sparse oblique trees}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11139},
}

@inproceedings{aaai2020_training_101941,
  title = {{Training Decision Trees as Replacement for Convolution Layers}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5801},
}

@inproceedings{icml2020_decision_38082,
  title = {{Decision Trees for Decision-Making under the Predict-then-Optimize Framework}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6758},
}

@inproceedings{icml2020_generalized_38247,
  title = {{Generalized and Scalable Optimal Sparse Decision Trees}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6340},
}

@inproceedings{aaai2022_fast_2091,
  title = {{Fast Sparse Decision Tree Optimization via Reference Ensembles}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/09604-fast-sparse-decision-tree-optimization-via-reference-ensembles/},
}

@inproceedings{aaai2019_weighted_101790,
  title = {{Weighted Oblique Decision Trees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4505},
}

@inproceedings{icml2018_binary_36550,
  title = {{Binary Partitions with Approximate Minimum Impurity}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1929},
}

@inproceedings{aaai2018_mdp_102512,
  title = {{MDP-Based Cost Sensitive Classification Using Decision Trees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11779},
}

@inproceedings{aaai2016_sparse_105075,
  title = {{Sparse Perceptron Decision Tree for Millions of Dimensions}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10247},
}

@inproceedings{aaai2021_optimal_1121,
  title = {{Optimal Decision Trees for Nonlinear Metrics}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/03733-optimal-decision-trees-for-nonlinear-metrics/},
}

@inproceedings{aaai2024_symbolic_7456,
  title = {{Symbolic Regression Enhanced Decision Trees for Classification Tasks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29091},
}

@inproceedings{aaai2019_constraint_100606,
  title = {{Constraint-Based Sequential Pattern Mining with Decision Diagrams}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/3962},
}

@inproceedings{aaai2023_optimal_4309,
  title = {{Optimal Decision Diagrams for Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25920},
}

@inproceedings{aaai2021_parameterized_1150,
  title = {{Parameterized Complexity of Small Decision Tree Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/06454-parameterized-complexity-of-small-decision-tree-learning/},
}

@inproceedings{neurips2019_optimal_58765,
  title = {{Optimal Sparse Decision Trees}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14552},
}

@inproceedings{aaai2024_generative_6130,
  title = {{Generative Model for Decision Trees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30104},
}

@inproceedings{aaai2024_robust_7173,
  title = {{Robust Loss Functions for Training Decision Trees with Noisy Labels}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29516},
}

@inproceedings{aaai2024_learning_6484,
  title = {{Learning Small Decision Trees with Few Outliers: A Parameterized Perspective}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29098},
}

@inproceedings{aaai2024_maptree_6563,
  title = {{MAPTree: Beating “Optimal” Decision Trees with Bayesian Decision Trees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28751},
}

@inproceedings{icml2024_fully_44885,
  title = {{Fully-Dynamic Approximate Decision Trees With Worst-Case Update Time Guarantees}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33567},
}

@inproceedings{icml2025_leveraging_46517,
  title = {{Leveraging Predictive Equivalence in Decision Trees}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46051},
}

@inproceedings{aaai2026_dfdt_107231,
  title = {{DFDT: Dynamic Fast Decision Tree for IoT Data Stream Mining on Edge Devices}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39583},
}

@inproceedings{aaai2025_extracting_9097,
  title = {{Extracting PAC Decision Trees from Black Box Binary Classifiers: The Gender Bias Study Case on BERT-based Language Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34177},
}

@inproceedings{icml2024_learning_44827,
  title = {{Learning Decision Trees and Forests with Algorithmic Recourse}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33625},
}

@inproceedings{icml2025_near_46381,
  title = {{Near-Optimal Decision Trees in a SPLIT Second}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46182},
}

@inproceedings{icml2025_optimal_48259,
  title = {{Optimal Decision Tree Pruning Revisited: Algorithms and Complexity}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44353},
}

@inproceedings{icml2026_from_52098,
  title = {{From Rashomon Theory to PRAXIS: Efficient Decision Tree Rashomon Sets}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Sgwd0l1u2V},
}

@inproceedings{icml2026_falling_52300,
  title = {{Falling Trees: A Model Class for Interpretable Risk Prioritization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=UWLHtRaVHp},
}

@inproceedings{neurips2025_differentiable_76057,
  title = {{Differentiable Decision Tree via "ReLU+Argmin" Reformulation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=F11iEhKoYp},
}

@inproceedings{neurips2025_sorted_76203,
  title = {{SORTeD Rashomon Sets of Sparse Decision Trees: Anytime Enumeration}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Gibq7Wa7Bq},
}

@inproceedings{neurips2025_empowering_79451,
  title = {{Empowering Decision Trees via Shape Function Branching}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=pZM3BsyzbM},
}

@inproceedings{neurips2025_learning_79608,
  title = {{Learning Gradient Boosted Decision Trees with Algorithmic Recourse}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=r9zzTQLnxw},
}

@inproceedings{iclr2026_active_86221,
  title = {{Active Learning for Decision Trees with Provable Guarantees}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=NOkjJPJIit},
}

@inproceedings{iclr2026_treegrad_86319,
  title = {{TreeGrad-Ranker: Feature Ranking via \$O(L)\$-Time Gradients for Decision Trees}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=OcMeNbkN13},
}