
%% Book List

@book{book/West2001,
    author = {West, Douglas B},
    year = {2001},
    publisher = {Prentice Hall},
    address = {Upper Saddle River},
    volume = {2},
    title = {Introduction to Graph Theory (2nd Edition)}
}

@book{book/Vershynin2018,
    author = {Roman Vershynin},
    year = {2018},
    publisher = {Cambridge university press},
    volume = {47},
    title = {High-Dimensional Probability: An Introduction with Applications in Data Science}
}

@book{book/Foss2011,
    title={An Introduction to Heavy-Tailed and Subexponential Distributions},
    author={Serguei Foss and Dmitry Korshunov and Stan Zachary},
    publisher = {Springer},
    year={2011}
}

@book{book/Lattimore2020,
    author = {Tor Lattimore and Csaba Szepesv{\'{a}}ri },
    year = {2020},
    publisher = {Cambridge university press},
    title = {Bandit Algorithms}
}

%% Journal List

% Mathematics
@article{journals/Thompson1933,
  title={ON THE LIKELIHOOD THAT ONE UNKNOWN PROBABILITY EXCEEDS ANOTHER IN VIEW OF THE EVIDENCE OF TWO SAMPLES},
  author={William R. Thompson},
  journal={Biometrika},
  year={1933},
  volume={25},
  pages={285-294}
}

@article{journals/Erdos1960,
    author = {P. Erd{\H o}s and A R{\'e}nyi},
    title = {On the Evolution of Random Graphs},
    journal = {Publication of the Mathematical Institute of the Hungarian Academy of Sciences},
    year = {1960},
    pages = {17--61},
    volume = {5},
}

@article{journals/Hoeffding1963,
    author = { Wassily Hoeffding },
    title = {Probability Inequalities for Sums of Bounded Random Variables},
    journal = {Journal of the American Statistical Association},
    volume = {58},
    number = {301},
    pages = {13-30},
    year  = {1963},
}

@article{journals/pieee/Shao1993,
  author    = {Min Shao and Chrysostomos L. Nikias},
  title     = {Signal processing with fractional lower order moments: stable processes and their applications},
  journal   = {Proceedings of the {IEEE}},
  volume    = {81},
  number    = {7},
  pages     = {986--1010},
  year      = {1993},
}

% Full-Information
@article{journals/iandc/Littlestone1994,
  author    = {Nick Littlestone and Manfred K. Warmuth},
  title     = {The Weighted Majority Algorithm},
  journal   = {Information and Computation},
  volume    = {108},
  number    = {2},
  pages     = {212--261},
  year      = {1994},
}

@article{journals/jacm/Cesa-Bianchi1997,
  author    = {Nicol{\`{o}} Cesa{-}Bianchi and Yoav Freund and David Haussler and David P. Helmbold and Robert E. Schapire and Manfred K. Warmuth},
  title     = {How to use expert advice},
  journal   = {Journal of {ACM}},
  volume    = {44},
  number    = {3},
  pages     = {427--485},
  year      = {1997}
}

% Median of Means
@article{journals/jcss/Alon1999,
  author    = {Noga Alon and Yossi Matias and Mario Szegedy},
  title     = {The Space Complexity of Approximating the Frequency Moments},
  journal   = {Journal of Computer and System Sciences},
  volume    = {58},
  number    = {1},
  pages     = {137--147},
  year      = {1999},
}

% Multi-Armed Bandits
@article{journals/Robbins1952,
  title={Some aspects of the sequential design of experiments},
  author={Herbert E. Robbins},
  journal={Bulletin of the American Mathematical Society},
  year={1952},
  volume={58},
  pages={527-535}
}

@article{journals/Lai1985,
author = {T.L Lai and Herbert Robbins}, 
title = {Asymptotically efficient adaptive allocation rules},
journal = {Advances in Applied Mathematics},
volume = {6},
number = {1},
pages = {4-22},
year = {1985}
}

@article{journals/Agrawal1995, 
author={Agrawal, Rajeev}, 
year={1995}, 
title={Sample mean based index policies by ${O}(\log n)$ regret for the multi-armed bandit problem},
volume={27}, 
number={4}, 
journal={Advances in Applied Probability}, 
pages={1054–1078}
}

@article{journals/ml/Auer2002a,
author = {Auer, Peter and Cesa-Bianchi, Nicol\`{o} and Fischer, Paul},
title = {Finite-Time Analysis of the Multiarmed Bandit Problem},
year = {2002},
volume = {47},
number = {2–3},
journal = {Machine Learning},
pages = {235–256},
}

@article{journals/jmlr/Auer2002b,
  title={Using Confidence Bounds for Exploitation-Exploration Trade-offs},
  author={Peter Auer},
  journal={Journal of Machine Learning Research},
  year={2002},
  volume={3},
  pages={397-422}
}

@article{journals/pmh/Auer2010,
        title={ {UCB} revisited: Improved regret bounds for the stochastic multi-armed bandit problem },
        author={ Peter Auer and Ronald Ortner },
        journal={ Periodica Mathematica Hungarica },
        year={ 2010 },
        volume={ 61 },
        pages={ 55-65 },
        number={ 1 },
}

% Action Elimination
@article{journals/jmlr/Even-Dar2006,
    author = {Even-Dar, Eyal and Mannor, Shie and Mansour, Yishay},
    title = {Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems},
    year = {2006},
    volume = {7},
    journal = {Journal of Machine Learning Research},
    pages = {1079–1105},
}

% Stochastic Linear Bandits
@article{journals/Rusmevichientong2010,
    author    = {Paat Rusmevichientong and John N. Tsitsiklis},
    title     = {Linearly Parameterized Bandits},
    journal   = {Mathematics of Operations Research},
    volume    = {35},
    number    = {2},
    pages     = {395--411},
    year      = {2010},
}

% Heavy-Tailed Bandits
@article{journals/Catoni2012,
     author = {Catoni, Olivier},
     title = {Challenging the empirical mean and empirical variance: {A} deviation study},
     journal = {Annales de l'Institut Henri Poincar\'e. Probabilit\'es et statistiques},
     pages = {1148--1185},
     volume = {48},
     number = {4},
     year = {2012}
}

@article{journals/tit/Bubeck2013,
    author={Bubeck, Sébastien and Cesa-Bianchi, Nicolò and Lugosi, Gábor},
    journal={IEEE Transactions on Information Theory}, 
    title={Bandits With Heavy Tail}, 
    year={2013},
    volume={59},
    number={11},
    pages={7711-7717},
}

% Survey
@article{journals/Bubeck2012Survey,
    author    = {S{\'{e}}bastien Bubeck and Nicol{\`{o}} Cesa{-}Bianchi},
    title     = {Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems},
    journal   = {Foundations and Trends in Machine Learning},
    volume    = {5},
    number    = {1},
    pages     = {1--122},
    year      = {2012},
}

% Adversary Graphical Bandits
@article{journals/corr/Alon2014,
  author    = {Noga Alon and Nicol{\`{o}} Cesa{-}Bianchi and Claudio Gentile and Shie Mannor and Yishay Mansour and Ohad Shamir},
  title     = {Nonstochastic Multi-Armed Bandits with Graph-Structured Feedback},
  journal   = {CoRR},
  volume    = {abs/1409.8428},
  year      = {2014},
}

% Examples
@article{journals/network/Mahanti2013,
  author    = {Aniket Mahanti and Niklas Carlsson and Anirban Mahanti and Martin F. Arlitt and Carey Williamson},
  title     = {A tale of the tails: Power-laws in internet measurements},
  journal   = {{IEEE} Network},
  volume    = {27},
  number    = {1},
  pages     = {59--64},
  year      = {2013}
}

@article{journals/Villar2015,
    author = {Sof{\'i}a S. Villar and Jack Bowden and James Wason},
    title = {{Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges}},
    volume = {30},
    journal = {Statistical Science},
    number = {2},
    pages = {199 -- 215},
    year = {2015},
}

@article{journals/Schwartz2017,
    author = {Schwartz, Eric M. and Bradlow, Eric T. and Fader, Peter S.},
    title = {Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments},
    journal = {Marketing Science},
    volume = {36},
    number = {4},
    pages = {500-522},
    year = {2017},
}

%% Conference List

% Full-Information
@inproceedings{conf/colt/Vovk1990,
  author    = {V. G. Vovk},
  title     = {Aggregating Strategies},
  booktitle = {Proceedings of the 3rd Annual Conference on Learning Theory},
  pages     = {371--386},
  publisher = {Morgan Kaufmann},
  year      = {1990}
}

% Stochastic Linear Bandits
@inproceedings{conf/icml/Abe1999,
author = {Abe, Naoki and Long, Philip M.},
title = {Associative Reinforcement Learning Using Linear Probabilistic Concepts},
year = {1999},
booktitle = {Proceedings of the 16th International Conference on Machine Learning},
pages = {3–11},
}

@inproceedings{conf/colt/Dani2008,
  author    = {Varsha Dani and Thomas P. Hayes and Sham M. Kakade},
  title     = {Stochastic Linear Optimization under Bandit Feedback},
  booktitle = {Proceedings of the 21st Annual Conference on Learning Theory},
  pages     = {355--366},
  year      = {2008},
}

@inproceedings{conf/www/Li2010,
author = {Li, Lihong and Chu, Wei and Langford, John and Schapire, Robert E.},
title = {A Contextual-Bandit Approach to Personalized News Article Recommendation},
year = {2010},
booktitle = {Proceedings of the 19th International Conference on World Wide Web},
pages = {661–670},
}

@inproceedings{conf/aistats/Chu2011,
  author    = {Wei Chu and Lihong Li and Lev Reyzin and Robert E. Schapire},
  title     = {Contextual Bandits with Linear Payoff Functions},
  booktitle = {Proceedings of the 14th International Conference on Artificial Intelligence and Statistics},
  pages     = {208--214},
  year      = {2011},
}

@inproceedings{conf/neurips/Abbasi-Yadkori2011,
  author    = {Yasin Abbasi{-}Yadkori and D{\'{a}}vid P{\'{a}}l and Csaba Szepesv{\'{a}}ri},
  title     = {Improved Algorithms for Linear Stochastic Bandits},
  booktitle = {Advances in Neural Information Processing Systems 24},
  pages     = {2312--2320},
  year      = {2011}
}

% Thompson Sampling
@inproceedings{conf/neurips/Chapelle2011,
author = {Chapelle, Olivier and Li, Lihong},
title = {An Empirical Evaluation of Thompson Sampling},
year = {2011},
booktitle = {Advances in Neural Information Processing Systems 24},
pages = {2249–2257},
}

@inproceedings{conf/colt/Agrawal2012,
  author    = {Shipra Agrawal and Navin Goyal},
  title     = {Analysis of Thompson Sampling for the Multi-armed Bandit Problem},
  booktitle = {Proceedings of the 25th Annual Conference on Learning Theory},
  pages     = {39.1--39.26},
  year      = {2012},
}

% KL-UCB
@inproceedings{conf/colt/Garivier2011,
  author={Aur{\'e}lien Garivier and Olivier Capp{\'e}},
  title={The {KL-UCB} Algorithm for Bounded Stochastic Bandits and Beyond},
  booktitle={Proceedings of the 24th Annual Conference on Learning Theory},
  year={2011},
  pages = {359–376},
}


% Adversarial Graphical Bandits
@inproceedings{conf/neurips/Mannor2011,
  author    = {Shie Mannor and Ohad Shamir},
  title     = {From Bandits to Experts: On the Value of Side-Observations},
  booktitle = {Advances in Neural Information Processing Systems 24},
  pages     = {684--692},
  year      = {2011},
}


% Stochastic Graphical Bandits(UCB)
@inproceedings{conf/uai/Caron2012,
author = {Caron, St\'{e}phane and Kveton, Branislav and Lelarge, Marc and Bhagat, Smriti},
title = {Leveraging Side Observations in Stochastic Bandits},
year = {2012},
booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence},
pages = {142–151},
}

@inproceedings{conf/uai/Hu2019,
  author    = {Bingshan Hu and Nishant A. Mehta and Jianping Pan},
  title     = {Problem-dependent Regret Bounds for Online Learning with Feedback Graphs},
  booktitle = {Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence},
  pages     = {852--861},
  year      = {2019},
}

% Stochastic Graphical Bandits(SE)
@inproceedings{conf/sigmetrics/Buccapatnam2014,
author = {Buccapatnam, Swapna and Eryilmaz, Atilla and Shroff, Ness B.},
title = {Stochastic Bandits with Side Observations on Networks},
year = {2014},
booktitle = {Proceedings of the 2014 ACM International Conference on Measurement and Modeling of Computer Systems},
pages = {289–300},
}

@inproceedings{conf/icml/Cohen2016,
  author    = {Alon Cohen and Tamir Hazan and Tomer Koren},
  title     = {Online Learning with Feedback Graphs Without the Graphs},
  booktitle = {Proceedings of the 33rd International Conference on Machine Learning},
  pages     = {811--819},
  year      = {2016},
}

@inproceedings{conf/alt/Lykouris2020,
  author    = {Thodoris Lykouris and {\'{E}}va Tardos and Drishti Wali},
  title     = {Feedback graph regret bounds for {T}hompson {S}ampling and {UCB}},
  booktitle = {Proceedings of the 31st International Conference on Algorithmic Learning Theory},
  pages     = {592--614},
  year      = {2020},
}

@inproceedings{conf/aaai/Lu2021b,
    author = {Shiyin Lu and Yao Hu and Lijun Zhang},
    title = {Stochastic Bandits with Graph Feedback in Non-Stationary Environments},
    booktitle = {Proceedings of the 35th AAAI Conference on Artificial Intelligence},
    year = {2021},
    pages = {8758--8766},
}

@article{journals/jmlr/Buccapatnam2017,
    author    = {Swapna Buccapatnam and Fang Liu and Atilla Eryilmaz and Ness B. Shroff},
    title     = {Reward Maximization Under Uncertainty: Leveraging Side-Observations on Networks},
    journal   = {Journal of Machine Learning Research},
    volume    = {18},
    number    = {216},
    pages     = {1--34},
    year      = {2017},
}

@article{journals/corr/Marinov2022,
  author    = {Teodor V. Marinov and
               Mehryar Mohri and
               Julian Zimmert},
  title     = {Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic
               Optimality},
  journal   = {CoRR},
  volume    = {abs/2206.10022},
  year      = {2022}
}

% Stochastic Graphical Bandits(TS)
@inproceedings{conf/aaai/Tossou2017,
  author    = {Aristide C. Y. Tossou and Christos Dimitrakakis and Devdatt P. Dubhashi},
  title     = {Thompson Sampling for Stochastic Bandits with Graph Feedback},
  booktitle = {Proceedings of the 31st {AAAI} Conference on Artificial Intelligence},
  pages     = {2660--2666},
  year      = {2017},
}

@inproceedings{conf/aaai/Liu2018a,
  author    = {Fang Liu and Swapna Buccapatnam and Ness B. Shroff},
  title     = {Information Directed Sampling for Stochastic Bandits With Graph Feedback},
  booktitle = {Proceedings of the 32nd {AAAI} Conference on Artificial Intelligence},
  pages     = {3643--3650},
  year      = {2018a},
}

@inproceedings{conf/uai/Liu2018b,
  author    = {Fang Liu and Zizhan Zheng and Ness B. Shroff},
  title     = {Analysis of Thompson Sampling for Graphical Bandits Without the Graphs},
  booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial},
  pages     = {13--22},
  year      = {2018b},
}

% Open Problem
@inproceedings{conf/colt/problem/Teodor2022,
  author    = {Marinov, Teodor Vanislavov and Mohri, Mehryar and Zimmert, Julian},
  title     = {Open Problem: Finite-Time Instance Dependent Optimality for Stochastic Online Learning with Feedback Graphs},
  booktitle = {Proceedings of 35th Conference on Learning Theory},
  pages     = {5644--5649},
  year      = {2022},
}

% Heavy-Tailed Bandits
@inproceedings{book/Rachev2003,
  title={Handbook of heavy tailed distributions in finance},
  author={Svetlozar T. Rachev},
  booktitle={Elsevier Monographs},
  year={2003}
}

@inproceedings{conf/allerton/Liu2011,
  author    = {Keqin Liu and Qing Zhao},
  title     = {Multi-armed bandit problems with heavy-tailed reward distributions},
  booktitle = {Proceedings of the 49th Annual Allerton Conference on Communication, Control, and Computing},
  pages     = {485--492},
  year      = {2011},
}

@inproceedings{conf/icml/Sajed2019,
  author    = {Touqir Sajed and Or Sheffet},
  title     = {An Optimal Private Stochastic-MAB Algorithm based on Optimal Private Stopping Rule},
  booktitle = {Proceedings of the 36th International Conference on Machine Learning},
  pages     = {5579--5588},
  year      = {2019},
}

@inproceedings{conf/aistats/Tao2022,
  author    = {Youming Tao and Yulian Wu and Peng Zhao and Di Wang},
  title     = {Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits},
  booktitle = {Proceedings of the 25th International Conference on Artificial Intelligence and Statistics},
  pages     = {1546--1574},
  year      = {2022},
}

@inproceedings{conf/neurips/Zhang2018a,
    author = {Lijun Zhang and Zhi-Hua Zhou},
    title = {$\ell_1$-regression with Heavy-tailed Distributions},
    booktitle = {Advances in Neural Information Processing Systems 31},
    pages = {1076--1086},
    year = {2018},
}

@inproceedings{conf/icml/Lu2019,
    author = {Shiyin Lu and Guanghui Wang and Yao Hu and Lijun Zhang},
    title = {Optimal Algorithms for {L}ipschitz Bandits with Heavy-tailed Rewards},
    booktitle = {Proceedings of the 36th International Conference on Machine Learning},
    pages = {4154--4163},
    year = {2019},
}

@article{journals/aos/Brownlees2015,
  title={Empirical risk minimization for heavy-tailed losses},
  author={ Brownlees, Christian  and  Joly, Emilien  and Lugosi, Gábor},
  journal={The Annals of Statistics},
  volume={43},
  number={6},
  year={2015},
}

@article{journals/jmlr/Hsu2016,
  author       = {Daniel J. Hsu and
                  Sivan Sabato},
  title        = {Loss Minimization and Parameter Estimation with Heavy Tails},
  journal      = {Journal of Machine Learning Research},
  volume       = {17},
  pages        = {18:1--18:40},
  year         = {2016},
}

% Heavy-Tailed Linear Bandits
@inproceedings{conf/icml/Medina2016,
  author    = {Andres Mu{\~{n}}oz Medina and Scott Yang},
  title     = {No-Regret Algorithms for Heavy-Tailed Linear Bandits},
  booktitle = {Proceedings of the 33rd International Conference on Machine Learning},
  pages     = {1642--1650},
  year      = {2016},
}

@inproceedings{conf/neurips/Shao2018,
  author    = {Han Shao and Xiaotian Yu and Irwin King and Michael R. Lyu},
  title     = {Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs},
  booktitle = {Advances in Neural Information Processing Systems 31},
  pages     = {8430--8439},
  year      = {2018},
}

@inproceedings{conf/ijcai/Xue2020,
  author    = {Bo Xue and Guanghui Wang and Yimu Wang and Lijun Zhang},
  title     = {Nearly Optimal Regret for Stochastic Linear Bandits with Heavy-Tailed Payoffs},
  booktitle = {Proceedings of the 29th International Joint Conference on Artificial Intelligence},
  pages     = {2936--2942},
  year      = {2020},
}

@inproceedings{conf/neurips/Kveton2015,
  author    = {Branislav Kveton and Zheng Wen and Azin Ashkan and Csaba Szepesv{\'{a}}ri},
  title     = {Combinatorial Cascading Bandits},
  booktitle = {Advances in Neural Information Processing Systems 28},
  pages     = {1450--1458},
  year      = {2015}
}

% Examples

@inproceedings{conf/cosn/Park2013,
  author    = {Jaimie Yejean Park and Kyoung{-}Won Lee and Sangyeon Kim and Chin{-}Wan Chung},
  title     = {Ads by whom? ads about what?: exploring user influence and contents in social advertising},
  booktitle = {Proceedings of the 1st ACM Conference on Online Social Networks},
  pages     = {155--164},
  year      = {2013},
}

@inproceedings{conf/icml/Chen2013,
  author    = {Wei Chen and Yajun Wang and Yang Yuan},
  title     = {Combinatorial Multi-Armed Bandit: General Framework and Applications},
  booktitle = {Proceedings of the 30th International Conference on Machine Learning},
  pages     = {151--159},
  year      = {2013},
}

@inproceedings{conf/neurips/Xu2013,
  author    = {Min Xu and Tao Qin and Tie{-}Yan Liu},
  title     = {Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising},
  booktitle = {Advances in Neural Information Processing Systems 26},
  pages     = {2400--2408},
  year      = {2013},
}

@inproceedings{conf/uai/Lattimore2014,
  author    = {Tor Lattimore and Koby Crammer and Csaba Szepesv{\'{a}}ri},
  title     = {Optimal Resource Allocation with Semi-Bandit Feedback},
  booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial
               Intelligence},
  pages     = {477--486},
  year      = {2014},
}

@inproceedings{conf/miccai/Gutierrez2017,
  author    = {Benjam{\'{\i}}n Guti{\'{e}}rrez and Lo{\"{\i}}c Peter and Tassilo Klein and Christian Wachinger},
  title = {A Multi-armed Bandit to Smartly Select a Training Set from Big Medical Data},
  booktitle     = {Proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages     = {38--45},
  year      = {2017}
}



