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

@inproceedings{neurips2018_dags_56989,
  title = {{DAGs with NO TEARS: Continuous Optimization for Structure Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11901},
}

@inproceedings{icml2023_deep_41571,
  title = {{Deep Graph Representation Learning and Optimization for Influence Maximization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24512},
}

@inproceedings{icml2016_learning_35881,
  title = {{Learning Granger Causality for Hawkes Processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/xuc16.html},
}

@inproceedings{aaai2020_global_103038,
  title = {{Global Greedy Dependency Parsing}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6348},
}

@inproceedings{aaai2018_a_99833,
  title = {{A Network-Specific Markov Random Field Approach to Community Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11281},
}

@inproceedings{neurips2017_experimental_56303,
  title = {{Experimental Design for Learning Causal Graphs with Latent Variables}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9468},
}

@inproceedings{neurips2018_contextual_56975,
  title = {{Contextual Stochastic Block Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11820},
}

@inproceedings{aaai2023_data_3402,
  title = {{Data Imputation with Iterative Graph Reconstruction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26348},
}

@inproceedings{icml2017_local_36275,
  title = {{Local-to-Global Bayesian Network Structure Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/688},
}

@inproceedings{icml2017_cost_36120,
  title = {{Cost-Optimal Learning of Causal Graphs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/747},
}

@inproceedings{aaai2023_opinion_4307,
  title = {{Opinion Optimization in Directed Social Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25585},
}

@inproceedings{iclr2023_learning_33001,
  title = {{Learning to Induce Causal Structure}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11132},
}

@inproceedings{neurips2019_deep_58112,
  title = {{Deep imitation learning for molecular inverse problems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13643},
}

@inproceedings{neurips2019_constraint_58035,
  title = {{Constraint-based Causal Structure Learning with Consistent Separating Sets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14348},
}

@inproceedings{aaai2016_generating_104961,
  title = {{Generating CP-Nets Uniformly at Random}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10115},
}

@inproceedings{aaai2024_graph_6150,
  title = {{Graph Bayesian Optimization for Multiplex Influence Maximization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30255},
}

@inproceedings{neurips2021_graphical_63458,
  title = {{Graphical Models in Heavy-Tailed Markets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27885},
}

@inproceedings{aaai2020_a_100241,
  title = {{A Graph Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5414},
}

@inproceedings{aaai2019_counting_103306,
  title = {{Counting and Sampling Markov Equivalent Directed Acyclic Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4799},
}

@inproceedings{neurips2018_direct_57047,
  title = {{Direct Estimation of Differences in Causal Graphs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11376},
}

@inproceedings{aaai2024_the_7550,
  title = {{The Irrelevance of Influencers: Information Diffusion with Re-Activation and Immunity Lasts Exponentially Long on Social Network Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29687},
}

@inproceedings{neurips2024_differentiable_70988,
  title = {{Differentiable Structure Learning with Partial Orders}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96218},
}

@inproceedings{aaai2024_efficient_5802,
  title = {{Efficient Axiomatization of OWL 2 EL Ontologies from Data by Means of Formal Concept Analysis}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28930},
}

@inproceedings{aaai2024_root_7191,
  title = {{Root Cause Explanation of Outliers under Noisy Mechanisms}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30035},
}

@inproceedings{icml2024_mgit_44304,
  title = {{MGit: A Model Versioning and Management System}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34148},
}

@inproceedings{iclr2026_bogrape_87832,
  title = {{BoGrape: Bayesian optimization over graphs with shortest-path encoded}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=g0EbJrKFQJ},
}

@inproceedings{aaai2026_experimentation_107505,
  title = {{Experimentation on Endogenous Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39857},
}

@inproceedings{aaai2025_diffim_8729,
  title = {{DiffIM: Differentiable Influence Minimization with Surrogate Modeling and Continuous Relaxation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33994},
}

@inproceedings{aaai2025_from_9252,
  title = {{From Your Block to Our Block: How to Find Shared Structure Between Stochastic Block Models over Multiple Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33305},
}

@inproceedings{aaai2025_individually_9559,
  title = {{Individually Stable Dynamics in Coalition Formation over Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33512},
}

@inproceedings{icml2024_scalable_43468,
  title = {{Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34984},
}

@inproceedings{icml2024_identifiability_44078,
  title = {{Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34374},
}

@inproceedings{icml2025_learn_46156,
  title = {{Learn to Vaccinate: Combining Structure Learning and Effective Vaccination for Epidemic and Outbreak Control}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46404},
}

@inproceedings{icml2026_graph_50258,
  title = {{Graph-GRPO: Training Graph Flow Models with Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Abko94Sl7D},
}

@inproceedings{icml2026_reduction_51237,
  title = {{Reduction of Probabilistic Chemical Reaction Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=K8TajLXmiD},
}

@inproceedings{icml2026_tn_51427,
  title = {{TN-SHAP-G: Graph-Structured Tensor Network Surrogates for Shapley Values and Interactions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=M4bE60aa1C},
}

@inproceedings{icml2026_causcale_52271,
  title = {{CauScale: Neural Causal Discovery at Scale}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=UCoD9hpiJs},
}

@inproceedings{neurips2025_graph_76459,
  title = {{Graph-based Symbolic Regression with Invariance and Constraint Encoding}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=JYB6wFcbky},
}

@inproceedings{neurips2025_practical_77077,
  title = {{Practical Bayes-Optimal Membership Inference Attacks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=QLQqMndNoL},
}