% Key papers for Causal and structured bandits (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2016_fairness_55722,
  title = {{Fairness in Learning: Classic and Contextual Bandits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/8516},
}

@inproceedings{neurips2016_causal_55618,
  title = {{Causal Bandits: Learning Good Interventions via Causal Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7058},
}

@inproceedings{neurips2016_multiple_55884,
  title = {{Multiple-Play Bandits in the Position-Based Model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/6951},
}

@inproceedings{aaai2019_efficient_101673,
  title = {{Efficient Counterfactual Learning from Bandit Feedback}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4388},
}

@inproceedings{neurips2018_fighting_57136,
  title = {{Fighting Boredom in Recommender Systems with Linear Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11189},
}

@inproceedings{icml2019_on_37604,
  title = {{On the Design of Estimators for Bandit Off-Policy Evaluation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4218},
}

@inproceedings{icml2020_doubly_38133,
  title = {{Doubly robust off-policy evaluation with shrinkage}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6423},
}

@inproceedings{neurips2020_inference_60081,
  title = {{Inference for Batched Bandits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18113},
}

@inproceedings{icml2016_the_36006,
  title = {{The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/wangb16.html},
}

@inproceedings{aaai2019_dynamic_101508,
  title = {{Dynamic Learning of Sequential Choice Bandit Problem under Marketing Fatigue}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4198},
}

@inproceedings{neurips2019_no_58679,
  title = {{No-Regret Learning in Unknown Games with Correlated Payoffs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14296},
}

@inproceedings{aaai2021_learning_909,
  title = {{Learning from eXtreme Bandit Feedback}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/08732-learning-from-extreme-bandit-feedback/},
}

@inproceedings{iclr2019_off_28945,
  title = {{Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/873},
}

@inproceedings{neurips2021_off_63386,
  title = {{Off-Policy Risk Assessment in Contextual Bandits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27848},
}

@inproceedings{neurips2018_factored_57121,
  title = {{Factored Bandits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11290},
}

@inproceedings{neurips2021_causal_61490,
  title = {{Causal Bandits with Unknown Graph Structure}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28604},
}

@inproceedings{iclr2018_residual_28512,
  title = {{Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://openreview.net/forum?id=HJNMYceCW},
}

@inproceedings{icml2018_causal_36566,
  title = {{Causal Bandits with Propagating Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2074},
}

@inproceedings{icml2021_dynamic_39275,
  title = {{Dynamic Balancing for Model Selection in Bandits and RL}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9907},
}

@inproceedings{icml2021_on_39712,
  title = {{On the Optimality of Batch Policy Optimization Algorithms}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9753},
}

@inproceedings{neurips2024_causal_70841,
  title = {{Causal Bandits for Linear Structural Equation Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/98317},
}

@inproceedings{aaai2024_robustly_7182,
  title = {{Robustly Improving Bandit Algorithms with Confounded and Selection Biased Offline Data: A Causal Approach}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30027},
}

@inproceedings{iclr2024_neural_34913,
  title = {{Neural Active Learning Beyond Bandits}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18161},
}

@inproceedings{neurips2024_partial_74281,
  title = {{Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93277},
}

@inproceedings{neurips2024_sequential_74285,
  title = {{Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93273},
}

@inproceedings{iclr2024_variance_35373,
  title = {{Variance-aware Regret Bounds for Stochastic Contextual Dueling Bandits}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17700},
}

@inproceedings{icml2024_causal_44496,
  title = {{Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33956},
}

@inproceedings{icml2025_clustering_46313,
  title = {{Clustering Items through Bandit Feedback: Finding the Right Feature out of Many}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46248},
}

@inproceedings{icml2025_stronger_48143,
  title = {{Stronger Neyman Regret Guarantees for Adaptive Experimental Design}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44466},
}

@inproceedings{icml2025_combinatorial_48695,
  title = {{Combinatorial Reinforcement Learning with Preference Feedback}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43927},
}

@inproceedings{icml2026_neural_55094,
  title = {{Neural Logistic Bandits}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=vT5PBMLBvx},
}

@inproceedings{neurips2024_off_70784,
  title = {{Off-policy estimation with adaptively collected data: the power of online learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96345},
}

@inproceedings{neurips2024_no_72992,
  title = {{No-Regret Bandit Exploration based on Soft Tree Ensemble Model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94426},
}

@inproceedings{neurips2024_is_73395,
  title = {{Is O(log N) practical? Near-Equivalence Between Delay Robustness and Bounded Regret in Bandits and RL}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94060},
}

@inproceedings{neurips2024_bandits_73633,
  title = {{Bandits with Abstention under Expert Advice}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93858},
}

@inproceedings{neurips2024_bandits_74407,
  title = {{Bandits with Preference Feedback: A Stackelberg Game Perspective}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93162},
}

@inproceedings{neurips2025_improved_75005,
  title = {{Improved Regret and Contextual Linear Extension for Pandora's Box and Prophet Inequality}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=3GPB0zbcSi},
}

@inproceedings{neurips2025_oracle_75334,
  title = {{Oracle-Efficient Combinatorial Semi-Bandits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=70wK1HKZPp},
}

@inproceedings{neurips2025_provably_80288,
  title = {{Provably Efficient Multi-Task Meta Bandit Learning via Shared Representations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=y9zhXirhCa},
}

@inproceedings{iclr2025_learning_83540,
  title = {{Learning from Imperfect Human Feedback: A Tale from Corruption-Robust Dueling}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28276},
}