% Key papers for Oracle-efficient contextual bandits (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2017_provably_36353,
  title = {{Provably Optimal Algorithms for Generalized Linear Contextual Bandits}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/654},
}

@inproceedings{neurips2018_online_57475,
  title = {{Online Learning with an Unknown Fairness Metric}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11268},
}

@inproceedings{icml2016_bistro_35750,
  title = {{BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/rakhlin16.html},
}

@inproceedings{neurips2016_linear_55850,
  title = {{Linear Contextual Bandits with Knapsacks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7392},
}

@inproceedings{icml2020_neural_38501,
  title = {{Neural Contextual Bandits with UCB-based Exploration}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6187},
}

@inproceedings{neurips2018_differentially_57037,
  title = {{Differentially Private Contextual Linear Bandits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11425},
}

@inproceedings{neurips2017_multi_56489,
  title = {{Multi-Task Learning for Contextual Bandits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9261},
}

@inproceedings{icml2018_practical_36926,
  title = {{Practical Contextual Bandits with Regression Oracles}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2475},
}

@inproceedings{aaai2019_balanced_101529,
  title = {{Balanced Linear Contextual Bandits}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4221},
}

@inproceedings{neurips2019_model_58613,
  title = {{Model Selection for Contextual Bandits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14630},
}

@inproceedings{icml2016_efficient_35811,
  title = {{Efficient Algorithms for Adversarial Contextual Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/syrgkanis16.html},
}

@inproceedings{neurips2017_conservative_56208,
  title = {{Conservative Contextual Linear Bandits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9172},
}

@inproceedings{icml2017_safety_36380,
  title = {{Safety-Aware Algorithms for Adversarial Contextual Bandit}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/628},
}

@inproceedings{icml2020_beyond_37975,
  title = {{Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6110},
}

@inproceedings{neurips2019_learning_58520,
  title = {{Learning in Generalized  Linear Contextual Bandits with Stochastic Delays}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14527},
}

@inproceedings{neurips2019_offline_58702,
  title = {{Offline Contextual Bandits with High Probability Fairness Guarantees}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14400},
}

@inproceedings{neurips2019_doubly_58168,
  title = {{Doubly-Robust Lasso Bandit}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13710},
}

@inproceedings{neurips2020_model_60337,
  title = {{Model Selection in Contextual Stochastic Bandit Problems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18831},
}

@inproceedings{aaai2018_online_102496,
  title = {{Online Clustering of Contextual Cascading Bandits}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11763},
}

@inproceedings{icml2017_optimal_36331,
  title = {{Optimal and Adaptive Off-policy Evaluation in Contextual Bandits}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/618},
}

@inproceedings{aaai2024_risk_7162,
  title = {{Risk-Aware Continuous Control with Neural Contextual Bandits}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30083},
}

@inproceedings{aaai2025_neural_10101,
  title = {{Neural Combinatorial Clustered Bandits for Recommendation Systems}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33692},
}

@inproceedings{neurips2024_robust_70595,
  title = {{Robust Neural Contextual Bandit against Adversarial Corruptions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96522},
}

@inproceedings{aaai2024_mixed_6653,
  title = {{Mixed-Effects Contextual Bandits}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29243},
}

@inproceedings{icml2024_a_43635,
  title = {{A Contextual Combinatorial Bandit Approach to Negotiation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34817},
}

@inproceedings{neurips2024_how_70317,
  title = {{How Does Variance Shape the Regret in Contextual Bandits?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96778},
}

@inproceedings{neurips2024_local_74108,
  title = {{Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93427},
}

@inproceedings{iclr2024_contextual_33654,
  title = {{Contextual Bandits with Online Neural Regression}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19421},
}

@inproceedings{icml2024_efficient_43280,
  title = {{Efficient Contextual Bandits with Uninformed Feedback Graphs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35172},
}

@inproceedings{icml2024_low_43837,
  title = {{Low-Rank Bandits via Tight Two-to-Infinity Singular Subspace Recovery}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34615},
}

@inproceedings{icml2024_fast_45643,
  title = {{Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32809},
}

@inproceedings{icml2025_catoni_46122,
  title = {{Catoni Contextual Bandits are Robust to Heavy-tailed Rewards}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46438},
}

@inproceedings{icml2025_nearly_46390,
  title = {{Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46174},
}

@inproceedings{icml2025_contextual_48125,
  title = {{Contextual Bandits for Unbounded Context Distributions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44484},
}

@inproceedings{icml2025_contextual_48184,
  title = {{Contextual Online Decision Making with Infinite-Dimensional Functional Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44427},
}

@inproceedings{icml2025_leveraging_48308,
  title = {{Leveraging Offline Data in Linear Latent Contextual Bandits}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44306},
}

@inproceedings{icml2025_high_48731,
  title = {{High Probability Bound for Cross-Learning Contextual Bandits with Unknown Context Distributions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43891},
}

@inproceedings{icml2026_contextual_49707,
  title = {{Contextual Slate GLM Bandits with Limited Adaptivity}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=4yLiQ3vcC9},
}

@inproceedings{icml2026_a_49938,
  title = {{A Direct Approach for Handling Contextual Bandits with Latent State Dynamics}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=7NA81PFi77},
}

@inproceedings{icml2026_optimal_50459,
  title = {{Optimal Regret for Policy Optimization in Contextual Bandits}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=CXsD6Ygc8e},
}