% Key papers for Bandit formulations for ML problems (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2017_automated_36074,
  title = {{Automated Curriculum Learning for Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/701},
}

@inproceedings{neurips2017_boltzmann_56184,
  title = {{Boltzmann Exploration Done Right}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9399},
}

@inproceedings{neurips2019_bootstrapping_57962,
  title = {{Bootstrapping Upper Confidence Bound}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14179},
}

@inproceedings{aaai2019_partially_101804,
  title = {{Partially Observable Multi-Sensor Sequential Change Detection: A Combinatorial Multi-Armed Bandit Approach}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4519},
}

@inproceedings{aaai2017_unimodal_104420,
  title = {{Unimodal Thompson Sampling for Graph-Structured Arms}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10797},
}

@inproceedings{neurips2016_optimistic_55926,
  title = {{Optimistic Gittins Indices}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7190},
}

@inproceedings{neurips2018_a_56787,
  title = {{A Bandit Approach to Sequential Experimental Design with False Discovery Control}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11366},
}

@inproceedings{neurips2019_machine_58568,
  title = {{Machine Teaching of Active Sequential Learners}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14114},
}

@inproceedings{neurips2020_banditpam_59428,
  title = {{BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17289},
}

@inproceedings{aaai2017_when_106046,
  title = {{When to Reset Your Keys: Optimal Timing of Security Updates via Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11033},
}

@inproceedings{aaai2019_crawling_99689,
  title = {{Crawling the Community Structure of Multiplex Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/3782},
}

@inproceedings{aaai2022_machine_2397,
  title = {{Machine Learning for Online Algorithm Selection under Censored Feedback}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/10370-machine-learning-for-online-algorithm-selection-under-censored-feedback/},
}

@inproceedings{icml2019_adaptive_37131,
  title = {{Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3590},
}

@inproceedings{icml2021_multi_39637,
  title = {{Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8749},
}

@inproceedings{icml2016_scalable_35973,
  title = {{Scalable Discrete Sampling as a Multi-Armed Bandit Problem}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/chenb16.html},
}

@inproceedings{neurips2021_a_63249,
  title = {{A Biased Graph Neural Network Sampler with Near-Optimal Regret}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28383},
}

@inproceedings{aaai2023_ac_3034,
  title = {{AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26456},
}

@inproceedings{aaai2023_robust_4552,
  title = {{Robust Planning over Restless Groups: Engagement Interventions for a Large-Scale Maternal Telehealth Program}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26672},
}

@inproceedings{icml2021_dichotomous_39232,
  title = {{Dichotomous Optimistic Search to Quantify Human Perception}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8599},
}

@inproceedings{icml2021_near_39647,
  title = {{Near-Optimal Confidence Sequences for Bounded Random Variables}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10463},
}

@inproceedings{neurips2024_reciprocal_73763,
  title = {{Reciprocal Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93740},
}

@inproceedings{iclr2024_constructing_35167,
  title = {{Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17906},
}

@inproceedings{icml2025_offline_46245,
  title = {{Offline Learning for Combinatorial Multi-armed Bandits}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46315},
}

@inproceedings{icml2025_tokenized_47408,
  title = {{Tokenized Bandit for LLM Decoding and Alignment}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45185},
}

@inproceedings{icml2025_banditspec_48149,
  title = {{BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44460},
}

@inproceedings{icml2026_kernelband_53196,
  title = {{KernelBand: Steering LLM-based Kernel Optimization via Hardware-Aware Multi-Armed Bandits}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=d8e2jBfMpE},
}

@inproceedings{neurips2024_mutli_72796,
  title = {{Mutli-Armed Bandits with Network Interference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94600},
}

@inproceedings{neurips2025_bandit_77832,
  title = {{Bandit Guided Submodular Curriculum for Adaptive Subset Selection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Y1UVWWWGKB},
}

@inproceedings{iclr2025_be_80611,
  title = {{Be More Diverse than the Most Diverse: Optimal Mixtures of Generative Models via Mixture-UCB Bandit Algorithms}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/31154},
}

@inproceedings{iclr2026_efficient_86110,
  title = {{Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=M0n3gtwHNg},
}

@inproceedings{cvpr2026_adaptive_97549,
  title = {{Adaptive Data Augmentation with Multi-armed Bandit: Sample-Efficient Embedding Calibration for Implicit Pattern Recognition}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Tang_Adaptive_Data_Augmentation_with_Multi-armed_Bandit_Sample-Efficient_Embedding_Calibration_for_CVPR_2026_paper.html},
}

@inproceedings{icml2025_adaptive_48501,
  title = {{Adaptive Data Collection for Robust Learning Across Multiple Distributions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44118},
}