@article{einstein,
author =       "Albert Einstein",
title =        "{Zur Elektrodynamik bewegter Körper}",
journal =      "Annalen der Physik",
volume =       "322",
number =       "10",
pages =        "891--921",
year =         "1905"}

@book{latexcompanion,
author    = "Michel Goossens and Frank Mittelbach and Alexander Samarin",
title     = "The \LaTeX\ Companion",
year      = "1993",
publisher = "Addison-Wesley"}

@article{komiyama2022minimax,
  title={Minimax optimal algorithms for fixed-budget best arm identification},
  author={Komiyama, Junpei and Tsuchiya, Taira and Honda, Junya},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={10393--10404},
  year={2022}
}


@article{gabillon2012best,
  title={Best arm identification: A unified approach to fixed budget and fixed confidence},
  author={Gabillon, Victor and Ghavamzadeh, Mohammad and Lazaric, Alessandro},
  journal={Advances in Neural Information Processing Systems},
  volume={25},
  year={2012}
}

@article{azizi2021fixed,
  title={Fixed-budget best-arm identification in structured bandits},
  author={Azizi, Mohammad Javad and Kveton, Branislav and Ghavamzadeh, Mohammad},
  journal={arXiv preprint arXiv:2106.04763},
  year={2021}
}
@inproceedings{kalyanakrishnan2012pac,
  title={{PAC} subset selection in stochastic multi-armed bandits.},
  author={Kalyanakrishnan, Shivaram and Tewari, Ambuj and Auer, Peter and Stone, Peter},
  booktitle={International Conference on Machine Learning},
  volume={12},
  pages={655--662},
  year={2012}
}

@misc{striatum2017,
  author       = {NTU Computational Lab},
  title        = {Striatum: A Contextual Bandit Library},
  year         = {2017},
  howpublished = {\url{https://github.com/ntucllab/striatum}},
  note         = {Accessed: 2025-06-02}
}

@article{kuroki2020polynomial,
  title={Polynomial-time algorithms for multiple-arm identification with full-bandit feedback},
  author={Kuroki, Yuko and Xu, Liyuan and Miyauchi, Atsushi and Honda, Junya and Sugiyama, Masashi},
  journal={Neural Computation},
  volume={32},
  number={9},
  pages={1733--1773},
  year={2020},
  publisher={MIT Press}
}



@article{bouneffouf2019survey,
  title={A survey on practical applications of multi-armed and contextual bandits},
  author={Bouneffouf, Djallel and Rish, Irina},
  journal={arXiv preprint arXiv:1904.10040},
  year={2019}
}


@article{kuleshov2014algorithms,
  title={Algorithms for multi-armed bandit problems},
  author={Kuleshov, Volodymyr and Precup, Doina},
  journal={arXiv preprint arXiv:1402.6028},
  year={2014}
}

@inproceedings{russo2016simple,
  title={Simple {Bayesian} algorithms for best arm identification},
  author={Russo, Daniel},
  booktitle={Conference on Learning Theory},
  pages={1417--1418},
  year={2016},
  organization={PMLR}
}

@article{kone2024pareto,
  title={Pareto Set Identification With Posterior Sampling},
  author={Kone, Cyrille and Jourdan, Marc and Kaufmann, Emilie},
  journal={arXiv preprint arXiv:2411.04939},
  year={2024}
}












@article{Hou23,
 author = {Yunlong Hou and Vincent Y. F. Tan and Zixin Zhong},
 journal = {IEEE Transactions on Information Theory},
 number = {4},
 pages = {2603--2634},
 publisher = {IEEE},
 title = {Almost Optimal Variance-Constrained Best Arm Identification},
 volume = {69},
 year = {2023}
}


@article{dc35850b-2ca1-314f-9e0d-470713436b17,
 ISSN = {00063444},
 URL = {http://www.jstor.org/stable/2332286},
 author = {William R. Thompson},
 journal = {Biometrika},
 number = {3/4},
 pages = {285--294},
 publisher = {[Oxford University Press, Biometrika Trust]},
 title = {On the Likelihood that One Unknown Probability Exceeds Another in View of the Evidence of Two Samples},
 urldate = {2024-06-25},
 volume = {25},
 year = {1933}
}



@article{jedra2020optimal,
  title={Optimal best-arm identification in linear bandits},
  author={Jedra, Yassir and Proutiere, Alexandre},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={10007--10017},
  year={2020}
}

@inproceedings{li2024optimal,
  title={Optimal Exploration is no harder than {Thompson} Sampling},
  author={Li, Zhaoqi and Jamieson, Kevin and Jain, Lalit},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={1684--1692},
  year={2024},
  organization={PMLR}
}



@article{de2014follow,
  title={Follow the leader if you can, hedge if you must},
  author={De Rooij, Steven and Van Erven, Tim and Gr{\"u}nwald, Peter D and Koolen, Wouter M},
  journal={The Journal of Machine Learning Research},
  volume={15},
  number={1},
  pages={1281--1316},
  year={2014},
  publisher={JMLR. org}
}

@article{maron1997racing,
  title={The Racing algorithm: Model selection for lazy learners},
  author={Maron, Oden and Moore, Andrew W},
  journal={Artificial Intelligence Review},
  volume={11},
  pages={193--225},
  year={1997},
  publisher={Springer}
}

@inproceedings{agrawal2013thompson,
  title={Thompson sampling for contextual bandits with linear payoffs},
  author={Agrawal, Shipra and Goyal, Navin},
  booktitle={International Conference on Machine Learning},
  pages={127--135},
  year={2013},
  organization={PMLR}
}



@inproceedings{lattimore2017end,
  title={The end of optimism? an asymptotic analysis of finite-armed linear bandits},
  author={Lattimore, Tor and Szepesvari, Csaba},
  booktitle={Artificial Intelligence and Statistics},
  pages={728--737},
  year={2017},
  organization={PMLR}
}




@article{faizal2022constrained,
  title={Constrained pure exploration multi-armed bandits with a fixed budget},
  author={Faizal, Fathima Zarin and Nair, Jayakrishnan},
  journal={arXiv preprint arXiv:2211.14768},
  year={2022}
}

@article{tang2024pure,
  title={Pure Exploration for Constrained Best Mixed Arm Identification with a Fixed Budget},
  author={Tang, Dengwang and Jain, Rahul and Nayyar, Ashutosh and Nuzzo, Pierluigi},
  journal={arXiv preprint arXiv:2405.15090},
  year={2024}
}

@inproceedings{audibert2010best,
  title={Best arm identification in multi-armed bandits},
  author={Audibert, Jean-Yves and Bubeck, S{\'e}bastien},
  booktitle={Proc. of 23rd  Conference on Learning Theory},
  pages={13},
  year={2010}
}
@article{yang2022minimax,
  title={Minimax optimal fixed-budget best arm identification in linear bandits},
  author={Yang, Junwen and Tan, Vincent},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={12253--12266},
  year={2022}
}

@inproceedings{karnin2013almost,
  title={Almost optimal exploration in multi-armed bandits},
  author={Karnin, Zohar and Koren, Tomer and Somekh, Oren},
  booktitle={International Conference on Machine Learning},
  pages={1238--1246},
  year={2013},
  organization={PMLR}
}
@inproceedings{zhong2021probabilistic,
  title={Probabilistic sequential shrinking: A best arm identification algorithm for stochastic bandits with corruptions},
  author={Zhong, Zixin and Cheung, Wang Chi and Tan, Vincent},
  booktitle={International Conference on Machine Learning},
  pages={12772--12781},
  year={2021},
  organization={PMLR}
}
@inproceedings{kirschner2018information,
  title={Information directed sampling and bandits with heteroscedastic noise},
  author={Kirschner, Johannes and Krause, Andreas},
  booktitle={Conference On Learning Theory},
  pages={358--384},
  year={2018},
  organization={PMLR}
}

@article{bian2024indexed,
  title={Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits},
  author={Bian, Jie and Tan, Vincent YF},
  journal={arXiv preprint arXiv:2405.15200},
  year={2024}
}
@inproceedings{kirschner2020information,
  title={Information directed sampling for linear partial monitoring},
  author={Kirschner, Johannes and Lattimore, Tor and Krause, Andreas},
  booktitle={Conference on Learning Theory},
  pages={2328--2369},
  year={2020},
  organization={PMLR}
}

@article{saber2021indexed,
  title={Indexed minimum empirical divergence for unimodal bandits},
  author={Saber, Hassan and M{\'e}nard, Pierre and Maillard, Odalric-Ambrym},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={7346--7356},
  year={2021}
}


@article{liu2024bypassing,
  title={Bypassing the simulator: Near-optimal adversarial linear contextual bandits},
  author={Liu, Haolin and Wei, Chen-Yu and Zimmert, Julian},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}













@article{abbasi2011improved,
  title={Improved algorithms for linear stochastic bandits},
  author={Abbasi-Yadkori, Yasin and P{\'a}l, D{\'a}vid and Szepesv{\'a}ri, Csaba},
  journal={Advances in Neural Information Processing Systems},
  volume={24},
  year={2011}
}

@book{lattimore2020bandit,
  title={Bandit Algorithms},
  author={Lattimore, Tor and Szepesv{\'a}ri, Csaba},
  year={2020},
  publisher={Cambridge University Press}
}

@article{honda2015non,
  title={Non-asymptotic analysis of a new bandit algorithm for semi-bounded rewards.},
  author={Honda, Junya and Takemura, Akimichi},
  journal={J. Mach. Learn. Res.},
  volume={16},
  pages={3721--3756},
  year={2015}
}



@inproceedings{li2010contextual,
  title={A contextual-bandit approach to personalized news article recommendation},
  author={Li, Lihong and Chu, Wei and Langford, John and Schapire, Robert E},
  booktitle={Proceedings of the 19th International Conference on World Wide Web},
  pages={661--670},
  year={2010}
}

@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},
  pages={235--256},
  year={2002},
  publisher={Springer}
}

@inproceedings{cantador2011second,
  title={{Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011)}},
  author={Cantador, Iv{\'a}n and Brusilovsky, Peter and Kuflik, Tsvi},
  booktitle={Proceedings of the Fifth ACM Conference on Recommender Systems},
  pages={387--388},
  year={2011}
}






@inproceedings{chu2011contextual,
  title={Contextual bandits with linear payoff functions},
  author={Chu, Wei and Li, Lihong and Reyzin, Lev and Schapire, Robert},
  booktitle={Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
  pages={208--214},
  year={2011},
  organization={JMLR Workshop and Conference Proceedings}
}


@inproceedings{bubeck2012towards,
  title={Towards minimax policies for online linear optimization with bandit feedback},
  author={Bubeck, S{\'e}bastien and Cesa-Bianchi, Nicolo and Kakade, Sham M},
  booktitle={Conference on Learning Theory},
  pages={41--1},
  year={2012},
  organization={JMLR Workshop and Conference Proceedings}
}






@inproceedings{gales2022norm,
  title={Norm-Agnostic Linear Bandits},
  author={Gales, Spencer B and Sethuraman, Sunder and Jun, Kwang-Sung},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={73--91},
  year={2022},
  organization={PMLR}
}


@inproceedings{valko2014spectral,
  title={Spectral bandits for smooth graph functions},
  author={Valko, Michal and Munos, R{\'e}mi and Kveton, Branislav and Koc{\'a}k, Tom{\'a}{\v{s}}},
  booktitle={International Conference on Machine Learning},
  pages={46--54},
  year={2014},
  organization={PMLR}
}










@inproceedings{garnett2011bayesian,
  title={Bayesian optimal active search on graphs},
  author={Garnett, Roman and Krishnamurthy, Yamuna and Wang, Donghan and Schneider, Jeff and Mann, Richard},
  booktitle={Ninth Workshop on Mining and Learning with Graphs},
  year={2011},
  organization={Citeseer}
}


@article{garnett2012bayesian,
  title={Bayesian optimal active search and surveying},
  author={Garnett, Roman and Krishnamurthy, Yamuna and Xiong, Xuehan and Schneider, Jeff and Mann, Richard},
  journal={arXiv preprint arXiv:1206.6406},
  year={2012}
}

@inproceedings{jiang2017efficient,
  title={Efficient nonmyopic active search},
  author={Jiang, Shali and Malkomes, Gustavo and Converse, Geoff and Shofner, Alyssa and Moseley, Benjamin and Garnett, Roman},
  booktitle={International Conference on Machine Learning},
  pages={1714--1723},
  year={2017}
}

@inproceedings{jiang2017contextual,
  title={Contextual decision processes with low bellman rank are pac-learnable},
  author={Jiang, Nan and Krishnamurthy, Akshay and Agarwal, Alekh and Langford, John and Schapire, Robert E},
  booktitle={International Conference on Machine Learning},
  pages={1704--1713},
  year={2017},
  organization={PMLR}
}

@inproceedings{jiang2018efficient,
  title={Efficient nonmyopic batch active search},
  author={Jiang, Shali and Malkomes, Gustavo and Abbott, Matthew and Moseley, Benjamin and Garnett, Roman},
  booktitle={Advances in Neural Information Processing Systems},
  pages={1099--1109},
  year={2018}
}
@article{browne2012survey,
  title={A survey of monte carlo tree search methods},
  author={Browne, Cameron B and Powley, Edward and Whitehouse, Daniel and Lucas, Simon M and Cowling, Peter I and Rohlfshagen, Philipp and Tavener, Stephen and Perez, Diego and Samothrakis, Spyridon and Colton, Simon},
  journal={IEEE Transactions on Computational Intelligence and AI in games},
  volume={4},
  number={1},
  pages={1--43},
  year={2012},
  publisher={IEEE}
}

@article{abramson1990expected,
  title={Expected-outcome: A general model of static evaluation},
  author={Abramson, Bruce},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={12},
  number={2},
  pages={182--193},
  year={1990},
  publisher={IEEE}
}

@article{kearns2002sparse,
  title={A sparse sampling algorithm for near-optimal planning in large Markov decision processes},
  author={Kearns, Michael and Mansour, Yishay and Ng, Andrew Y},
  journal={Machine learning},
  volume={49},
  number={2-3},
  pages={193--208},
  year={2002},
  publisher={Springer}
}

@inproceedings{even2002pac,
  title={PAC bounds for multi-armed bandit and Markov decision processes},
  author={Even-Dar, Eyal and Mannor, Shie and Mansour, Yishay},
  booktitle={International Conference on Computational Learning Theory},
  pages={255--270},
  year={2002},
  organization={Springer}
}

@inproceedings{kocsis2006bandit,
  title={Bandit based monte-carlo planning},
  author={Kocsis, Levente and Szepesv{\'a}ri, Csaba},
  booktitle={European conference on machine learning},
  pages={282--293},
  year={2006},
  organization={Springer}
}

@article{rosin2011multi,
  title={Multi-armed bandits with episode context},
  author={Rosin, Christopher D},
  journal={Annals of Mathematics and Artificial Intelligence},
  volume={61},
  number={3},
  pages={203--230},
  year={2011},
  publisher={Springer}
}
@article{amani2019linear,
  author    = {Sanae Amani and Mahnoosh Alizadeh and Christos Thrampoulidis},
  title     = {Linear stochastic bandits under safety constraints},
  journal   = {Advances in Neural Information Processing Systems},
  volume    = {32},
  year      = {2019}
}

@article{moradipari2021safe,
  author    = {Ahmadreza Moradipari and Sanae Amani and Mahnoosh Alizadeh and  Christos Thrampoulidis},
  title     = {Safe linear {Thompson} sampling with side information},
  journal   = {IEEE Transactions on Signal Processing},
  volume    = {69},
  pages     = {3755--3767},
  year      = {2021}
}

@article{liu2021efficient,
  author    = {Xin Liu and others},
  title     = {An efficient pessimistic-optimistic algorithm for stochastic linear bandits with general constraints},
  journal   = {Advances in Neural Information Processing Systems},
  volume    = {34},
  pages     = {24075--24086},
  year      = {2021}
}

@article{zhou2022kernelized,
  author    = {Xingyu Zhou and Bo Ji},
  title     = {On kernelized multi-armed bandits with constraints},
  journal   = {Advances in Neural Information Processing Systems},
  volume    = {35},
  pages     = {14--26},
  year      = {2022}
}

@article{pacchiano2024contextual,
  author    = {Aldo Pacchiano and Mohammad Ghavamzadeh and Peter Bartlett},
  title     = {Contextual bandits with stage-wise constraints},
  journal   = {arXiv preprint arXiv:2401.08016},
  year      = {2024}
}
@inproceedings{lindner2022interactively,
  author    = {David Lindner and others},
  title     = {Interactively learning preference constraints in linear bandits},
  booktitle = {International Conference on Machine Learning},
  series    = {PMLR},
  pages     = {13505--13527},
  year      = {2022}
}

@article{camilleri2022active,
  author    = {Romain Camilleri and others},
  title     = {Active learning with safety constraints},
  journal   = {Advances in Neural Information Processing Systems},
  volume    = {35},
  pages     = {33201--33214},
  year      = {2022}
}

@inproceedings{wang2022best,
  title={Best arm identification with safety constraints},
  author={Wang, Zhenlin and Wagenmaker, Andrew J and Jamieson, Kevin},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={9114--9146},
  year={2022},
  organization={PMLR}
}


@article{yang2025stochastically,
  title={Stochastically Constrained Best Arm Identification with {Thompson} Sampling},
  author={Yang, Le and Gao, Siyang and Li, Cheng and Wang, Yi},
  journal={Automatica},
volume = "176",
pages = "112223",
month = "Jun",
  year={2025}
}

@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}
}

@article{shang2023price,
  author    = {Xuedong Shang and Igor Colin and Merwan Barlier and Hamza Cherkaoui},
  title     = {Price of safety in linear best arm identification},
  journal   = {arXiv preprint arXiv:2309.08709},
  year      = {2023}
}
@inproceedings{katz2018feasible,
  title={Feasible arm identification},
  author={Katz-Samuels, Julian and Scott, Clay},
  booktitle={International Conference on Machine Learning},
  pages={2535--2543},
  year={2018},
  organization={PMLR}
}