

%%% Hetero & BO

@phdthesis{kirschner2021information,
  title={Information-Directed Sampling-Frequentist Analysis and Applications},
  author={Kirschner, Johannes},
  year={2021},
  school={ETH Zurich}
}

@article{cowen2020empirical,
  title={An Empirical Study of Assumptions in Bayesian Optimisation},
  author={Cowen-Rivers, Alexander I and Lyu, Wenlong and Tutunov, Rasul and Wang, Zhi and Grosnit, Antoine and Griffiths, Ryan Rhys and Jianye, Hao and Wang, Jun and Ammar, Haitham Bou},
  journal={arXiv preprint arXiv:2012.03826},
  year={2020}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
%%% Reference added by Shuang

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}

@article{wang2020residual,
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  year={2020}
}

@inproceedings{kveton2019perturbed,
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  year={2020},
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}

@article{kveton2019perturbedMAB,
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  journal={arXiv preprint arXiv:1902.10089},
  year={2019}
}


@book{lattimore2020bandit,
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}

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}

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@article{russo2017tutorial,
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}

@inproceedings{agrawal2013further,
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  organization={PMLR}
}

@inproceedings{agrawal2013thompson,
  title={Thompson sampling for contextual bandits with linear payoffs},
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}


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}

@article{thompson1933likelihood,
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@inproceedings{honda2014optimality,
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  year={2014},
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}

@inproceedings{riquelme2018deep,
  title={Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling},
  author={Riquelme, Carlos and Tucker, George and Snoek, Jasper},
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  year={2018}
}


@inproceedings{chu2011contextual,
  title={Contextual bandits with linear payoff functions},
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}

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@inproceedings{agarwal2009online,
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}

@inproceedings{li2010contextual,
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}

@inproceedings{tang2015personalized,
  title={Personalized recommendation via parameter-free contextual bandits},
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}

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@inproceedings{hao2020adaptive,
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}

@inproceedings{garivier2016optimal,
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}

@inproceedings{kveton2020randomized,
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}

@article{osband2015bootstrapped,
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  year={2015}
}

@article{vaswani2018new,
  title={New insights into bootstrapping for bandits},
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  year={2018}
}

@article{hao2019bootstrapping,
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  volume={32},
  year={2019}
}

@article{elmachtoub2017practical,
  title={A practical method for solving contextual bandit problems using decision trees},
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  year={2017}
}

@article{eckles2014thompson,
  title={Thompson sampling with the online bootstrap},
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  year={2014}
}

@article{osband2016deep,
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}

@article{langford2007epoch,
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  pages={96--1},
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@inproceedings{baransi2014sub,
  title={Sub-sampling for multi-armed bandits},
  author={Baransi, Akram and Maillard, Odalric-Ambrym and Mannor, Shie},
  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages={115--131},
  year={2014},
  organization={Springer}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55

%%% Motivation Articles




%%% Martingale Concentration toolbox
@book{jacod2013limit,
  title={Limit theorems for stochastic processes},
  author={Jacod, Jean and Shiryaev, Albert},
  volume={288},
  year={2013},
  publisher={Springer Science \& Business Media}
}

@article{auer2002finite,
  title={Finite-time analysis of the multiarmed bandit problem},
  author={Auer, Peter and Cesa-Bianchi, Nicolo and Fischer, Paul},
  journal={Machine learning},
  volume={47},
  number={2},
  pages={235--256},
  year={2002},
  publisher={Springer}
}