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

@inproceedings{neurips2017_lightgbm_56449,
  title = {{LightGBM: A Highly Efficient Gradient Boosting Decision Tree}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9099},
}

@inproceedings{icml2017_gradient_36208,
  title = {{Gradient Boosted Decision Trees for High Dimensional Sparse Output}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/870},
}

@inproceedings{icml2016_evasion_35824,
  title = {{Evasion and Hardening of Tree Ensemble Classifiers}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/kantchelian16.html},
}

@inproceedings{neurips2019_a_57798,
  title = {{A Debiased MDI Feature Importance Measure for Random Forests}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13879},
}

@inproceedings{neurips2023_when_67305,
  title = {{When Do Neural Nets Outperform Boosted Trees on Tabular Data?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/73658},
}

@inproceedings{cvpr2016_learning_11664,
  title = {{Learning Online Smooth Predictors for Realtime Camera Planning Using Recurrent Decision Trees}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{neurips2018_when_57779,
  title = {{When do random forests fail?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11304},
}

@inproceedings{iclr2021_are_30359,
  title = {{Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees?}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2911},
}

@inproceedings{neurips2017_cost_56223,
  title = {{Cost efficient gradient boosting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8946},
}

@inproceedings{aaai2020_regional_102250,
  title = {{Regional Tree Regularization for Interpretability in Deep Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6112},
}

@inproceedings{aaai2018_autoencoder_102465,
  title = {{AutoEncoder by Forest}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11732},
}

@inproceedings{aaai2022_trading_2873,
  title = {{Trading Complexity for Sparsity in Random Forest Explanations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/05461-trading-complexity-for-sparsity-in-random-forest-explanations/},
}

@inproceedings{icml2020_born_37988,
  title = {{Born-Again Tree Ensembles}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6682},
}

@inproceedings{icml2018_finding_36698,
  title = {{Finding Influential Training Samples for Gradient Boosted Decision Trees}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2395},
}

@inproceedings{icml2020_smaller_38767,
  title = {{Smaller, more accurate regression forests using tree alternating optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6675},
}

@inproceedings{cvpr2022_pushing_19637,
  title = {{Pushing the Envelope of Gradient Boosting Forests via Globally-Optimized Oblique Trees}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{icml2017_simultaneous_36394,
  title = {{Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/782},
}

@inproceedings{neurips2018_active_56831,
  title = {{Active Learning for Non-Parametric Regression Using Purely Random Trees}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11262},
}

@inproceedings{aaai2018_mercs_102468,
  title = {{MERCS: Multi-Directional Ensembles of Regression and Classification Trees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11735},
}

@inproceedings{neurips2019_sample_58941,
  title = {{Sample Efficient Active Learning of Causal Trees}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14350},
}

@inproceedings{aaai2024_mepsi_6571,
  title = {{MEPSI: An MDL-Based Ensemble Pruning Approach with Structural Information}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28984},
}

@inproceedings{aaai2025_free_9229,
  title = {{Free Lunch in the Forest: Functionally-Identical Pruning of Boosted Tree Ensembles}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33811},
}

@inproceedings{icml2025_low_46054,
  title = {{Low-distortion and GPU-compatible Tree Embeddings in Hyperbolic Space}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46503},
}

@inproceedings{aaai2024_invariant_6353,
  title = {{Invariant Random Forest: Tree-Based Model Solution for OOD Generalization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29283},
}

@inproceedings{aaai2025_accurate_7998,
  title = {{Accurate Estimation of Feature Importance Faithfulness for Tree Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33834},
}

@inproceedings{iclr2024_fast_34502,
  title = {{Fast Hyperboloid Decision Tree Algorithms}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18572},
}

@inproceedings{icml2024_multi_45223,
  title = {{Multi-group Learning for Hierarchical Groups}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33229},
}

@inproceedings{icml2025_mixed_49021,
  title = {{Mixed-curvature decision trees and random forests}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43603},
}

@inproceedings{icml2026_claritree_51207,
  title = {{CLARITree: Cholesky and Lookahead Accelerations for Regression with Interpretable Piecewise Linear Trees}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=JjBozF4i2w},
}

@inproceedings{icml2026_correcting_53443,
  title = {{Correcting Split Selection in Online Decision Trees via Anytime-Valid Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=fZvEZQWJrR},
}

@inproceedings{neurips2024_conditional_70525,
  title = {{Conditional Density Estimation with Histogram Trees}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96586},
}

@inproceedings{neurips2024_generative_73004,
  title = {{Generative Forests}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94417},
}

@inproceedings{neurips2025_discretization_75174,
  title = {{Discretization-free Multicalibration through Loss Minimization over Tree Ensembles}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=56Y2HRjPIp},
}

@inproceedings{neurips2025_the_77214,
  title = {{The Rashomon Set Has It All: Analyzing Trustworthiness of Trees under Multiplicity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=RXsDPn3toF},
}

@inproceedings{neurips2025_bridging_79622,
  title = {{Bridging Arbitrary and Tree Metrics via Differentiable Gromov Hyperbolicity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=rIudtwY0VM},
}

@inproceedings{neurips2025_a_79980,
  title = {{A faster training algorithm for regression trees with linear leaves, and an analysis of its complexity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=urDdBuhbLx},
}

@inproceedings{iclr2025_nrgboost_83978,
  title = {{NRGBoost: Energy-Based Generative Boosted Trees}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27846},
}

@inproceedings{iclr2026_hinge_84543,
  title = {{Hinge Regression Tree: A Newton Method for Oblique Regression Tree Splitting}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=3pZwJB6SX9},
}

@inproceedings{iclr2026_jacobian_88353,
  title = {{Jacobian Aligned Random Forests}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=lpYHuxPEXV},
}

@inproceedings{aaai2026_robust_106847,
  title = {{Robust Watermarking on Gradient Boosting Decision Trees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39199},
}