@inproceedings{korda2016distributed,
  title={Distributed clustering of linear bandits in peer to peer networks},
  author={Korda, Nathan and Szorenyi, Balazs and Li, Shuai},
  booktitle={International conference on machine learning},
  pages={1301--1309},
  year={2016},
  organization={PMLR}
}
@inproceedings{mahadik2020fast,
  title={Fast distributed bandits for online recommendation systems},
  author={Mahadik, Kanak and Wu, Qingyun and Li, Shuai and Sabne, Amit},
  booktitle={Proceedings of the 34th ACM international conference on supercomputing},
  pages={1--13},
  year={2020}
}


@inproceedings{ma2020off,
author = {Ma, Jiaqi and Zhao, Zhe and Yi, Xinyang and Yang, Ji and Chen, Minmin and Tang, Jiaxi and Hong, Lichan and Chi, Ed H.},
title = {Off-Policy Learning in Two-Stage Recommender Systems},
year = {2020},
isbn = {9781450370233},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3366423.3380130},
doi = {10.1145/3366423.3380130},
booktitle = {Proceedings of The Web Conference 2020},
pages = {463–473},
numpages = {11},
keywords = {Two-stage Systems, Recommender Systems, Off-policy Learning, Neural Networks},
location = {Taipei, Taiwan},
series = {WWW '20}
}


@inproceedings{tyler2010showing,
title	= {Showing Relevant Ads via Lipschitz Context Multi-Armed Bandits},
author	= {Tyler Lu and Dávid Pál and Martin Pál},
year	= {2010},
URL	= {http://jmlr.csail.mit.edu/proceedings/papers/v9/lu10a/lu10a.pdf},
booktitle	= {Thirteenth International Conference on Artificial Intelligence and Statistics}
}

@inproceedings{avadhanula2021stochastic,
author = {Avadhanula, Vashist and Colini Baldeschi, Riccardo and Leonardi, Stefano and Sankararaman, Karthik Abinav and Schrijvers, Okke},
title = {Stochastic Bandits for Multi-Platform Budget Optimization in Online Advertising},
year = {2021},
isbn = {9781450383127},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3442381.3450074},
doi = {10.1145/3442381.3450074},
pages = {2805–2817},
numpages = {13},
keywords = {Multi-armed Bandits, Online Advertising},
location = {Ljubljana, Slovenia},
series = {WWW '21}
}





@INPROCEEDINGS{bouneffouf2020survey,
  author={Bouneffouf, Djallel and Rish, Irina and Aggarwal, Charu},
  booktitle={2020 IEEE Congress on Evolutionary Computation (CEC)}, 
  title={Survey on Applications of Multi-Armed and Contextual Bandits}, 
  year={2020},
  volume={},
  number={},
  pages={1-8},
  doi={10.1109/CEC48606.2020.9185782}}

@inproceedings{glowacka2019bandit,
author = {Glowacka, Dorota},
title = {Bandit Algorithms in Recommender Systems},
year = {2019},
isbn = {9781450362436},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3298689.3346956},
doi = {10.1145/3298689.3346956},
pages = {574–575},
numpages = {2},
keywords = {exploration-exploitation trade-off, recommender systems, interactive search, bandit algorithms, information retrieval},
location = {Copenhagen, Denmark},
series = {RecSys '19}
}


@article{kale2010non,
  title={Non-stochastic bandit slate problems},
  author={Kale, Satyen and Reyzin, Lev and Schapire, Robert E},
  journal={Advances in Neural Information Processing Systems},
  volume={23},
  year={2010}
}
@article{cesa2012combinatorial,
  title={Combinatorial bandits},
  author={Cesa-Bianchi, Nicolo and Lugosi, G{\'a}bor},
  journal={Journal of Computer and System Sciences},
  volume={78},
  number={5},
  pages={1404--1422},
  year={2012},
  publisher={Elsevier}
}

@article{gai2012combinatorial,
  title={Combinatorial network optimization with unknown variables: Multi-armed bandits with linear rewards and individual observations},
  author={Gai, Yi and Krishnamachari, Bhaskar and Jain, Rahul},
  journal={IEEE/ACM Transactions on Networking},
  volume={20},
  number={5},
  pages={1466--1478},
  year={2012},
  publisher={IEEE}
}
@inproceedings{kveton2015tight,
  title={Tight regret bounds for stochastic combinatorial semi-bandits},
  author={Kveton, Branislav and Wen, Zheng and Ashkan, Azin and Szepesvari, Csaba},
  booktitle={Artificial Intelligence and Statistics},
  pages={535--543},
  year={2015},
  organization={PMLR}
}
@inproceedings{bubeck2013multiple,
  title={Multiple identifications in multi-armed bandits},
  author={Bubeck, S{\'e}ebastian and Wang, Tengyao and Viswanathan, Nitin},
  booktitle={International Conference on Machine Learning},
  pages={258--265},
  year={2013},
  organization={PMLR}
}
@article{chen2014combinatorial,
  title={Combinatorial pure exploration of multi-armed bandits},
  author={Chen, Shouyuan and Lin, Tian and King, Irwin and Lyu, Michael R and Chen, Wei},
  journal={Advances in neural information processing systems},
  volume={27},
  year={2014}
}
@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{audibert2010best,
  title={Best arm identification in multi-armed bandits.},
  author={Audibert, Jean-Yves and Bubeck, S{\'e}bastien and Munos, R{\'e}mi},
  booktitle={COLT},
  pages={41--53},
  year={2010}
}
@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{even2006action,
  title={Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems.},
  author={Even-Dar, Eyal and Mannor, Shie and Mansour, Yishay and Mahadevan, Sridhar},
  journal={Journal of machine learning research},
  volume={7},
  number={6},
  year={2006}
}
@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{kalyanakrishnan2010efficient,
  title={Efficient selection of multiple bandit arms: Theory and practice.},
  author={Kalyanakrishnan, Shivaram and Stone, Peter},
  booktitle={ICML},
  volume={10},
  pages={511--518},
  year={2010}
}
@inproceedings{kaufmann2013information,
  title={Information complexity in bandit subset selection},
  author={Kaufmann, Emilie and Kalyanakrishnan, Shivaram},
  booktitle={Conference on Learning Theory},
  pages={228--251},
  year={2013},
  organization={PMLR}
}
@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{chen2016pure,
  title={Pure exploration of multi-armed bandit under matroid constraints},
  author={Chen, Lijie and Gupta, Anupam and Li, Jian},
  booktitle={Conference on Learning Theory},
  pages={647--669},
  year={2016},
  organization={PMLR}
}
@article{bubeck2012regret,
  title={Regret analysis of stochastic and nonstochastic multi-armed bandit problems},
  author={Bubeck, S{\'e}bastien and Cesa-Bianchi, Nicolo and others},
  journal={Foundations and Trends{\textregistered} in Machine Learning},
  volume={5},
  number={1},
  pages={1--122},
  year={2012},
  publisher={Now Publishers, Inc.}
}
@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{hron2021component,
  title={On component interactions in two-stage recommender systems},
  author={Hron, Jiri and Krauth, Karl and Jordan, Michael and Kilbertus, Niki},
  journal={Advances in neural information processing systems},
  volume={34},
  pages={2744--2757},
  year={2021}
}
@article{jiang2017practical,
  title={Practical algorithms for best-k identification in multi-armed bandits},
  author={Jiang, Haotian and Li, Jian and Qiao, Mingda},
  journal={arXiv preprint arXiv:1705.06894},
  year={2017}
}
@article{wang2017improving,
  title={Improving regret bounds for combinatorial semi-bandits with probabilistically triggered arms and its applications},
  author={Wang, Qinshi and Chen, Wei},
  journal={Advances in Neural Information Processing Systems},
  volume={30},
  year={2017}
}

@article{DBLP:journals/corr/QinL13,
  author    = {Tao Qin and
               Tie{-}Yan Liu},
  title     = {Introducing {LETOR} 4.0 Datasets},
  journal   = {CoRR},
  volume    = {abs/1306.2597},
  year      = {2013},
  url       = {http://arxiv.org/abs/1306.2597},
  timestamp = {Mon, 01 Jul 2013 20:31:25 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/QinL13},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@article{chaudhuri2017online,
  title={Online learning to rank with top-k feedback},
  author={Chaudhuri, Sougata and Tewari, Ambuj},
  journal={The Journal of Machine Learning Research},
  volume={18},
  number={1},
  pages={3599--3648},
  year={2017},
  publisher={JMLR. org}
}
