@book{wald2004sequential,
  title={Sequential analysis},
  author={Wald, Abraham},
  year={2004},
  publisher={Courier Corporation}
}

@inproceedings{garivier2016optimal,
  title={Optimal best arm identification with fixed confidence},
  author={Garivier, Aur{\'e}lien and Kaufmann, Emilie},
  booktitle={Conference on Learning Theory},
  pages={998--1027},
  year={2016},
  organization={PMLR}
}

@inproceedings{kalyanakrishnan2012pac,
  title={PAC subset selection in stochastic multi-armed bandits.},
  author={Kalyanakrishnan, Shivaram and Tewari, Ambuj and Auer, Peter and Stone, Peter},
  booktitle={ICML},
  volume={12},
  pages={655--662},
  year={2012}
}

@inproceedings{jamieson2014lil,
  title={lil’ucb: An optimal exploration algorithm for multi-armed bandits},
  author={Jamieson, Kevin and Malloy, Matthew and Nowak, Robert and Bubeck, S{\'e}bastien},
  booktitle={Conference on Learning Theory},
  pages={423--439},
  year={2014},
  organization={PMLR}
}

@inproceedings{jamieson2014best,
  title={Best-arm identification algorithms for multi-armed bandits in the fixed confidence setting},
  author={Jamieson, Kevin and Nowak, Robert},
  booktitle={2014 48th annual conference on information sciences and systems (CISS)},
  pages={1--6},
  year={2014},
  organization={IEEE}
}

@article{cheung2024leveraging,
  title={Leveraging (Biased) Information: Multi-armed Bandits with Offline Data},
  author={Cheung, Wang Chi and Lyu, Lixing},
  journal={arXiv preprint arXiv:2405.02594},
  year={2024}
}

@misc{auer2002finite,
  title={Finite-time Analysis of the Multiarmed Bandit Problem},
  author={Auer, P},
  year={2002},
  publisher={Kluwer Academic Publishers}
}

@inproceedings{chen2017nearly,
  title={Nearly instance optimal sample complexity bounds for top-k arm selection},
  author={Chen, Lijie and Li, Jian and Qiao, Mingda},
  booktitle={Artificial Intelligence and Statistics},
  pages={101--110},
  year={2017},
  organization={PMLR}
}


@article{kaufmann2016complexity,
  title={On the complexity of best-arm identification in multi-armed bandit models},
  author={Kaufmann, Emilie and Capp{\'e}, Olivier and Garivier, Aur{\'e}lien},
  journal={The Journal of Machine Learning Research},
  volume={17},
  number={1},
  pages={1--42},
  year={2016},
  publisher={JMLR. org}
}

@article{blanchet2019robust,
  title={Robust Wasserstein profile inference and applications to machine learning},
  author={Blanchet, Jose and Kang, Yang and Murthy, Karthyek},
  journal={Journal of Applied Probability},
  volume={56},
  number={3},
  pages={830--857},
  year={2019},
  publisher={Cambridge University Press}
}

@article{chen2022data,
  title={Data-pooling reinforcement learning for personalized healthcare intervention},
  author={Chen, Xinyun and Shi, Pengyi and Pu, Shanwen},
  journal={arXiv preprint arXiv:2211.08998},
  year={2022}
}

@article{si2023distributionally,
  title={Distributionally robust batch contextual bandits},
  author={Si, Nian and Zhang, Fan and Zhou, Zhengyuan and Blanchet, Jose},
  journal={Management Science},
  volume={69},
  number={10},
  pages={5772--5793},
  year={2023},
  publisher={INFORMS}
}

@article{crammer2008learning,
  title={Learning from Multiple Sources.},
  author={Crammer, Koby and Kearns, Michael and Wortman, Jennifer},
  journal={Journal of machine learning research},
  volume={9},
  number={8},
  year={2008}
}


@inproceedings{wang2021multitask,
  title={Multitask bandit learning through heterogeneous feedback aggregation},
  author={Wang, Zhi and Zhang, Chicheng and Singh, Manish Kumar and Riek, Laurel and Chaudhuri, Kamalika},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={1531--1539},
  year={2021},
  organization={PMLR}
}

@article{besbes2022beyond,
  title={Beyond IID: data-driven decision-making in heterogeneous environments},
  author={Besbes, Omar and Ma, Will and Mouchtaki, Omar},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={23979--23991},
  year={2022}
}


@inproceedings{katz2019top,
  title={Top feasible arm identification},
  author={Katz-Samuels, Julian and Scott, Clayton},
  booktitle={The 22nd International Conference on Artificial Intelligence and Statistics},
  pages={1593--1601},
  year={2019},
  organization={PMLR}
}

@inproceedings{shivaswamy2012multi,
  title={Multi-armed bandit problems with history},
  author={Shivaswamy, Pannagadatta and Joachims, Thorsten},
  booktitle={Artificial Intelligence and Statistics},
  pages={1046--1054},
  year={2012},
  organization={PMLR}
}

@article{agrawal2023optimal,
  title={Optimal best-arm identification in bandits with access to offline data},
  author={Agrawal, Shubhada and Juneja, Sandeep and Shanmugam, Karthikeyan and Suggala, Arun Sai},
  journal={arXiv preprint arXiv:2306.09048},
  year={2023}
}


@inproceedings{bu2020online,
  title={Online pricing with offline data: Phase transition and inverse square law},
  author={Bu, Jinzhi and Simchi-Levi, David and Xu, Yunzong},
  booktitle={International Conference on Machine Learning},
  pages={1202--1210},
  year={2020},
  organization={PMLR}
}


@inproceedings{hao2023leveraging,
  title={Leveraging demonstrations to improve online learning: Quality matters},
  author={Hao, Botao and Jain, Rahul and Lattimore, Tor and Van Roy, Benjamin and Wen, Zheng},
  booktitle={International Conference on Machine Learning},
  pages={12527--12545},
  year={2023},
  organization={PMLR}
}


@inproceedings{wagenmaker2023leveraging,
  title={Leveraging offline data in online reinforcement learning},
  author={Wagenmaker, Andrew and Pacchiano, Aldo},
  booktitle={International Conference on Machine Learning},
  pages={35300--35338},
  year={2023},
  organization={PMLR}
}

@article{gur2022adaptive,
  title={Adaptive sequential experiments with unknown information arrival processes},
  author={Gur, Yonatan and Momeni, Ahmadreza},
  journal={Manufacturing \& Service Operations Management},
  volume={24},
  number={5},
  pages={2666--2684},
  year={2022},
  publisher={INFORMS}
}


@article{bouneffouf2019optimal,
  title={Optimal exploitation of clustering and history information in multi-armed bandit},
  author={Bouneffouf, Djallel and Parthasarathy, Srinivasan and Samulowitz, Horst and Wistub, Martin},
  journal={arXiv preprint arXiv:1906.03979},
  year={2019}
}


@article{ye2020combining,
  title={Combining offline causal inference and online bandit learning for data driven decision},
  author={Ye, Li and Lin, Yishi and Xie, Hong and Lui, John},
  journal={arXiv preprint arXiv:2001.05699},
  year={2020}
}


@inproceedings{tennenholtz2021bandits,
  title={Bandits with partially observable confounded data},
  author={Tennenholtz, Guy and Shalit, Uri and Mannor, Shie and Efroni, Yonathan},
  booktitle={Uncertainty in Artificial Intelligence},
  pages={430--439},
  year={2021},
  organization={PMLR}
}

@article{liu2025offline,
  title={Offline Learning for Combinatorial Multi-armed Bandits},
  author={Liu, Xutong and Dai, Xiangxiang and Zuo, Jinhang and Wang, Siwei and Wong, Carlee-Joe and Lui, John and Chen, Wei},
  journal={arXiv preprint arXiv:2501.19300},
  year={2025}
}


@article{russo2016information,
  title={An information-theoretic analysis of thompson sampling},
  author={Russo, Daniel and Van Roy, Benjamin},
  journal={Journal of Machine Learning Research},
  volume={17},
  number={68},
  pages={1--30},
  year={2016}
}


@article{russo2018learning,
  title={Learning to optimize via information-directed sampling},
  author={Russo, Daniel and Van Roy, Benjamin},
  journal={Operations Research},
  volume={66},
  number={1},
  pages={230--252},
  year={2018},
  publisher={INFORMS}
}


@inproceedings{liu2016prior,
  title={On the prior sensitivity of thompson sampling},
  author={Liu, Che-Yu and Li, Lihong},
  booktitle={International Conference on Algorithmic Learning Theory},
  pages={321--336},
  year={2016},
  organization={Springer}
}


@article{simchowitz2021bayesian,
  title={Bayesian decision-making under misspecified priors with applications to meta-learning},
  author={Simchowitz, Max and Tosh, Christopher and Krishnamurthy, Akshay and Hsu, Daniel J and Lykouris, Thodoris and Dudik, Miro and Schapire, Robert E},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={26382--26394},
  year={2021}
}


@article{zhang2019warm,
  title={Warm-starting contextual bandits: Robustly combining supervised and bandit feedback},
  author={Zhang, Chicheng and Agarwal, Alekh and Daum{\'e} III, Hal and Langford, John and Negahban, Sahand N},
  journal={arXiv preprint arXiv:1901.00301},
  year={2019}
}

@article{mansour2009domain,
  title={Domain adaptation: Learning bounds and algorithms},
  author={Mansour, Yishay and Mohri, Mehryar and Rostamizadeh, Afshin},
  journal={arXiv preprint arXiv:0902.3430},
  year={2009}
}

@article{ben2010theory,
  title={A theory of learning from different domains},
  author={Ben-David, Shai and Blitzer, John and Crammer, Koby and Kulesza, Alex and Pereira, Fernando and Vaughan, Jennifer Wortman},
  journal={Machine learning},
  volume={79},
  pages={151--175},
  year={2010},
  publisher={Springer}
}

@article{lai1985asymptotically,
  title={Asymptotically efficient adaptive allocation rules},
  author={Lai, Tze Leung and Robbins, Herbert},
  journal={Advances in applied mathematics},
  volume={6},
  number={1},
  pages={4--22},
  year={1985},
  publisher={Academic Press}
}