% Key papers for Best-of-both-worlds bandit algorithms (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2019_weighted_59195,
  title = {{Weighted Linear Bandits for Non-Stationary Environments}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14171},
}

@inproceedings{icml2017_the_36426,
  title = {{The Price of Differential Privacy for Online Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/582},
}

@inproceedings{icml2019_beating_37186,
  title = {{Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3810},
}

@inproceedings{neurips2020_learning_60200,
  title = {{Learning Strategy-Aware Linear Classifiers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17554},
}

@inproceedings{neurips2019_online_58747,
  title = {{Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13961},
}

@inproceedings{neurips2019_online_58749,
  title = {{Online EXP3 Learning in Adversarial Bandits with Delayed Feedback}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14125},
}

@inproceedings{aaai2018_information_102484,
  title = {{Information Directed Sampling for Stochastic Bandits With Graph Feedback}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11751},
}

@inproceedings{neurips2016_regret_55958,
  title = {{Regret of Queueing Bandits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/8499},
}

@inproceedings{aaai2017_thompson_104520,
  title = {{Thompson Sampling for Stochastic Bandits with Graph Feedback}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10897},
}

@inproceedings{neurips2020_bias_59464,
  title = {{Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17245},
}

@inproceedings{aaai2019_algorithms_100619,
  title = {{Algorithms for Average Regret Minimization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/3975},
}

@inproceedings{neurips2016_dueling_55694,
  title = {{Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/6957},
}

@inproceedings{aaai2022_a_1675,
  title = {{A Lyapunov-Based Methodology for Constrained Optimization with Bandit Feedback}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/03716-a-lyapunov-based-methodology-for-constrained-optimization-with-bandit-feedback/},
}

@inproceedings{icml2021_achieving_39014,
  title = {{Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9819},
}

@inproceedings{icml2021_on_39696,
  title = {{On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10639},
}

@inproceedings{neurips2019_combinatorial_58001,
  title = {{Combinatorial Bandits with Relative Feedback}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13326},
}

@inproceedings{icml2020_multinomial_38483,
  title = {{Multinomial Logit Bandit with Low Switching Cost}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5867},
}

@inproceedings{neurips2022_non_64803,
  title = {{Non-stationary Bandits with Knapsacks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54531},
}

@inproceedings{neurips2022_lipschitz_65480,
  title = {{Lipschitz Bandits with Batched Feedback}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53368},
}

@inproceedings{icml2019_online_37622,
  title = {{Online learning with kernel losses}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4222},
}

@inproceedings{iclr2024_bandits_35648,
  title = {{Bandits with Replenishable Knapsacks: the Best of both Worlds}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17425},
}

@inproceedings{neurips2024_improved_70126,
  title = {{Improved Analysis for Bandit Learning in Matching Markets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96952},
}

@inproceedings{neurips2024_optimal_70856,
  title = {{Optimal Clustering with Bandit Feedback}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/98332},
}

@inproceedings{neurips2024_a_71668,
  title = {{A Best-of-both-worlds Algorithm for Bandits with Delayed Feedback with Robustness to Excessive Delays}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95613},
}

@inproceedings{neurips2024_assouad_73389,
  title = {{Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94066},
}

@inproceedings{neurips2024_beyond_73451,
  title = {{Beyond Primal-Dual Methods in Bandits with Stochastic and Adversarial Constraints}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94015},
}

@inproceedings{aaai2025_improved_9521,
  title = {{Improved Regret Bounds for Online Fair Division with Bandit Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33541},
}

@inproceedings{aaai2025_online_10197,
  title = {{Online Nonsubmodular Optimization with Delayed Feedback in the Bandit Setting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35507},
}

@inproceedings{iclr2024_unified_34068,
  title = {{Unified Projection-Free Algorithms for Adversarial DR-Submodular Optimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19007},
}

@inproceedings{icml2024_testing_44486,
  title = {{Testing the Feasibility of Linear Programs with Bandit Feedback}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33966},
}

@inproceedings{icml2024_exploration_44918,
  title = {{Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33534},
}

@inproceedings{icml2025_learning_46067,
  title = {{Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46490},
}

@inproceedings{icml2025_beyond_46197,
  title = {{Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsity}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46363},
}

@inproceedings{icml2025_heavy_46315,
  title = {{Heavy-Tailed Linear Bandits: Huber Regression with One-Pass Update}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46246},
}

@inproceedings{icml2025_tracking_47597,
  title = {{Tracking The Best Expert Privately}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44999},
}

@inproceedings{icml2025_faster_48435,
  title = {{Faster Rates for Private Adversarial Bandits}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44182},
}

@inproceedings{icml2025_competing_48464,
  title = {{Competing Bandits in Matching Markets via Super Stability}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44153},
}

@inproceedings{icml2025_no_48977,
  title = {{No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43646},
}

@inproceedings{icml2025_principal_49079,
  title = {{Principal-Agent Bandit Games with Self-Interested and Exploratory Learning Agents}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43545},
}

@inproceedings{icml2025_adversarial_49158,
  title = {{Adversarial Combinatorial Semi-bandits with Graph Feedback}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43467},
}