% Key papers for Practical contextual bandit extensions (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2018_transferable_102432,
  title = {{Transferable Contextual Bandit for Cross-Domain Recommendation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11699},
}

@inproceedings{neurips2016_phased_55936,
  title = {{Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7029},
}

@inproceedings{aaai2020_contextual_102263,
  title = {{Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6125},
}

@inproceedings{neurips2020_adversarial_59335,
  title = {{Adversarial Attacks on Linear Contextual Bandits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17175},
}

@inproceedings{neurips2021_statistical_62296,
  title = {{Statistical Inference with M-Estimators on Adaptively Collected Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28600},
}

@inproceedings{icml2020_designing_38100,
  title = {{Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6292},
}

@inproceedings{neurips2017_interactive_56391,
  title = {{Interactive Submodular Bandit}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8812},
}

@inproceedings{neurips2021_post_61296,
  title = {{Post-Contextual-Bandit Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27658},
}

@inproceedings{aaai2024_using_7731,
  title = {{Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30328},
}

@inproceedings{aaai2020_improved_101951,
  title = {{Improved Algorithms for Conservative Exploration in Bandits}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5812},
}

@inproceedings{aaai2023_efficient_3550,
  title = {{Efficient Explorative Key-Term Selection Strategies for Conversational Contextual Bandits}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26225},
}

@inproceedings{neurips2021_efficient_61290,
  title = {{Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27215},
}

@inproceedings{icml2019_contextual_37250,
  title = {{Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3696},
}

@inproceedings{neurips2021_generalized_61898,
  title = {{Generalized Linear Bandits with Local Differential Privacy}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26985},
}

@inproceedings{aaai2018_constructive_102537,
  title = {{Constructive Preference Elicitation Over Hybrid Combinatorial Spaces}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11804},
}

@inproceedings{icml2018_mitigating_36852,
  title = {{Mitigating Bias in Adaptive Data Gathering via Differential Privacy}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2339},
}

@inproceedings{neurips2021_risk_61375,
  title = {{Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/25948},
}

@inproceedings{neurips2024_direct_73918,
  title = {{Direct Preference-Based Evolutionary Multi-Objective Optimization with Dueling Bandits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93593},
}

@inproceedings{neurips2021_design_61945,
  title = {{Design of Experiments for Stochastic Contextual Linear Bandits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27127},
}

@inproceedings{neurips2022_conformal_64536,
  title = {{Conformal Off-Policy Prediction in Contextual Bandits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54058},
}

@inproceedings{aaai2025_stochastic_10843,
  title = {{Stochastic Online Instrumental Variable Regression: Regrets for Endogeneity and Bandit Feedback}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33778},
}

@inproceedings{icml2026_contextual_55200,
  title = {{Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=weMYE1B16x},
}

@inproceedings{neurips2024_contextual_71144,
  title = {{Contextual Decision-Making with Knapsacks Beyond the Worst Case}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96079},
}

@inproceedings{neurips2024_rome_73150,
  title = {{RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94281},
}

@inproceedings{aaai2024_improving_6297,
  title = {{Improving Health Information Access in the World’s Largest Maternal Mobile Health Program via Bandit Algorithms}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30329},
}

@inproceedings{aaai2024_responsible_7124,
  title = {{Responsible Bandit Learning via Privacy-Protected Mean-Volatility Utility}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30182},
}

@inproceedings{aaai2025_cost_8545,
  title = {{Cost-Aware Near-Optimal Policy Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35027},
}

@inproceedings{aaai2025_offline_10158,
  title = {{Offline-to-Online Hyperparameter Transfer for Stochastic Bandits}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34243},
}

@inproceedings{iclr2024_cascading_34184,
  title = {{Cascading Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18891},
}

@inproceedings{icml2024_refining_44157,
  title = {{Refining Minimax Regret for Unsupervised Environment Design}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34295},
}

@inproceedings{icml2024_randomized_45721,
  title = {{Randomized Confidence Bounds for Stochastic Partial Monitoring}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32731},
}

@inproceedings{icml2025_online_46649,
  title = {{Online Clustering of Dueling Bandits}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45922},
}

@inproceedings{icml2025_linear_48091,
  title = {{Linear Contextual Bandits With Interference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44517},
}

@inproceedings{icml2025_a_48560,
  title = {{A Classification View on Meta Learning Bandits}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44061},
}

@inproceedings{icml2026_incentivized_51369,
  title = {{Incentivized Exploration with Stochastic Covariates: A Two-Stage Mechanism Design for Recommender System}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=LTHHiPNbrs},
}

@inproceedings{icml2026_linear_54997,
  title = {{Linear Bandits beyond Inner Product Spaces, the case of Bandit Optimal Transport}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=ugjBMARbyt},
}

@inproceedings{neurips2024_gaussian_70488,
  title = {{Gaussian Process Bandits for Top-k Recommendations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96620},
}

@inproceedings{neurips2024_provably_71886,
  title = {{Provably Efficient Interactive-Grounded Learning with Personalized Reward}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95417},
}