@article{Distill2011,
  title={Distilling the wisdom of crowds: weighted aggregation of decisions on multiple issues},
  author={Baharad, Eyal and Goldberger, Jacob and Koppel, Moshe and Nitzan, Shmuel},
  journal={Autonomous Agents and Multi-Agent Systems},
  year={2011}
}
%  volume={22},
%  number={1},
%  pages={31--42},
%publisher={Springer}

@inproceedings{HosseiniM0S21,
  author    = {Hadi Hosseini and
               Debmalya Mandal and
               Nisarg Shah and
               Kevin Shi},
  title     = {Surprisingly Popular Voting Recovers Rankings, Surprisingly!},
  booktitle = {{IJCAI}},
  pages     = {245--251},
  publisher = {ijcai.org},
  year      = {2021}
}

@inproceedings{FaliszewskiST20,
  author    = {Piotr Faliszewski and
               Arkadii Slinko and
               Nimrod Talmon},
  title     = {Multiwinner Rules with Variable Number of Winners},
  booktitle = {{ECAI}},
  series    = {Frontiers in Artificial Intelligence and Applications},
  volume    = {325},
  pages     = {67--74},
  publisher = {{IOS} Press},
  year      = {2020}
}


@article{LacknerS20,
  author    = {Martin Lackner and
               Piotr Skowron},
  title     = {Approval-Based Committee Voting: Axioms, Algorithms, and Applications},
  journal   = {CoRR},
  volume    = {abs/2007.01795},
  year      = {2020},
  url       = {https://arxiv.org/abs/2007.01795},
  eprinttype = {arXiv},
  eprint    = {2007.01795},
  timestamp = {Fri, 09 Oct 2020 09:46:56 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2007-01795.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@Article{approval2016,
  author={D. Marc Kilgour},
  title={{Approval elections with a variable number of winners}},
  journal={Theory and Decision},
  year=2016,
  volume={81},
  number={2},
  pages={199-211},
  month={August},
  keywords={Approval voting; Multi-winner elections; VNW elections; Hall of fame election; Net approval},
  doi={10.1007/s11238-016-9535-2},
  abstract={ Multi-winner elections, for example, the election of members to a committee, are now quite common, and include the interesting subclass of elections with a variable number of winners, or VNW elections. In VNW elections, voters determine how many winners there are, as well as which candidates win. Common VNW elections include elections to bestow honorary status, such as enshrinement in a hall of fame, and elections to determine a shortlist of, say, job candidates for interviews. Such elections are VNW elections whenever the number of winners is not specified in advance. Multi-winner elections are often conducted by adapting standard procedures for single-winner elections. Approval balloting is particularly appropriate for multi-winner elections, as every ballot, and the set of winners, are subsets of the set of all candidates. Many methods of counting approval ballots appropriate to VNW elections are reviewed and illustrated, and their properties—desirable and undesirable—discussed.},
  url={https://ideas.repec.org/a/kap/theord/v81y2016i2d10.1007_s11238-016-9535-2.html}
}


@article{size2009,
  author    = {Jorge Alcalde{-}Unzu and
               Marc Vorsatz},
  title     = {Size approval voting},
  journal   = {J. Econ. Theory},
  volume    = {144},
  number    = {3},
  pages     = {1187--1210},
  year      = {2009},
  url       = {https://doi.org/10.1016/j.jet.2008.08.002},
  doi       = {10.1016/j.jet.2008.08.002},
  timestamp = {Mon, 24 Feb 2020 15:54:40 +0100},
  biburl    = {https://dblp.org/rec/journals/jet/Alcalde-UnzuV09.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}



@article{solution2017,
  title={A solution to the single-question crowd wisdom problem},
  author={Prelec, Dra{\v{z}}en and Seung, H Sebastian and McCoy, John},
  journal={Nature},
  volume={541},
  number={7638},
  pages={532--535},
  year={2017},
  publisher={Nature Publishing Group}
}


@article{collective2017,
  title={Collective decision making and jury theorems},
  author={Nitzan, Shmuel and Paroush, Jacob},
  journal={The Oxford Handbook of Law and Economics},
  volume={1},
  year={2017}
}

@inproceedings{size2020,
author = {Xiao, Tao and Sikdar, Sujoy},
title = {Size-Relaxed Committee Selection under the Chamberlin-Courant Rule},
year = {2020},
isbn = {9781450375184},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
address = {Richland, SC},
abstract = {The Chamberlin-Courant(CC) family of committee selection rules aim to select a committee of size k from a set of m candidates to maximize the satisfaction of n agents. The satisfaction of an agent from a committee depends only on the rank of her favorite candidate and is determined by a satisfaction function. Unfortunately, computing an optimal committee of size k is hard in general, which has led to the development of approximation algorithms that select a committee of size k, which guarantees some fraction of the optimal satisfaction. However, there is often some flexibility in the size of the committee to be selected.In this paper, we initiate the study of size-relaxed committee selection for the family of cc rules. Our main results are polynomial-time algorithms to select committees of size at most k• O(\l{}log n), whose satisfaction is guaranteed to be at least that of the optimal committee of size k, and show that this is tight. We also provide a constant-factor approximation algorithm for a class of approval ballot based CC rules.},
booktitle = {Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems},
pages = {1530–1538},
numpages = {9},
keywords = {approximation algorithms, size-relaxed committee selection, chamberlin-courant rule},
location = {Auckland, New Zealand},
series = {AAMAS '20}
}

@inproceedings{Minimax2017,
  title={A minimax optimal algorithm for crowdsourcing},
  author={Bonald, Thomas and Combes, Richard},
booktitle={NIPS},
  year={2017}
}
%  booktitle={Advances in Neural Information Processing Systems},
%  pages={4352--4360},

@inproceedings{weighted2014,
  title={A weighted aggregation rule in crowdsourcing systems for high result accuracy},
  author={Yue, Dejun and Yu, Ge and Shen, Derong and Yu, Xiaocong},
  booktitle={IEEE International Conference on Dependable, Autonomic and Secure Computing},
  year={2014}
  }
%  booktitle={2014 IEEE 12th International Conference on Dependable, Autonomic and Secure Computing},
%  pages={265--270},
%  organization={IEEE}


@article{domain2019,
  title={Domain-weighted majority voting for crowdsourcing},
  author={Tao, Dapeng and Cheng, Jun and Yu, Zhengtao and Yue, Kun and Wang, Lizhen},
  journal={IEEE transactions on neural networks and learning systems},
  year={2018}
  }
%volume={30},
%  number={1},
%  pages={163--174},
%  publisher={IEEE}

@InProceedings{Wisdom2014,
author = {Yu, Dian and Huang, Hongzhao and Cassidy, Taylor and Ji, Heng and Wang, Chi and Zhi, Shi and Han, Jiawei and Voss, Clare and Magdon-Ismail, Malik},
title = {The Wisdom of Minority: Unsupervised Slot Filling Validation based on Multi-dimensional Truth-Finding},
booktitle = {Proceedings of 2014 International Conference on Computational Linguistics},
year = {2014},
month = {August},
url = {https://www.microsoft.com/en-us/research/publication/the-wisdom-of-minority-unsupervised-slot-filling-validation-based-on-multi-dimensional-truth-finding/},
edition = {COLING'14},
}

@article{truth2019,
  author    = {Reshef Meir and
               Ofra Amir and
               Gal Cohensius and
               Omer Ben{-}Porat and
               Lirong Xia},
  title     = {Truth Discovery via Proxy Voting},
  journal = {arXiv:1905.00629},
  year      = {2019}
}
%  journal   = {CoRR},
%  volume    = {abs/1905.00629},
%  url       = {http://arxiv.org/abs/1905.00629},
%  archivePrefix = {arXiv},
%  eprint    = {1905.00629},
%  timestamp = {Mon, 27 May 2019 13:15:00 +0200},
%  biburl    = {https://dblp.org/rec/journals/corr/abs-1905-00629.bib},
%  bibsource = {dblp computer science bibliography, https://dblp.org}

@inproceedings{multidimensional2010,
  author    = {Peter Welinder and
               Steve Branson and
               Serge J. Belongie and
               Pietro Perona},
  title     = {The Multidimensional Wisdom of Crowds},
  booktitle = {NIPS},
   year      = {2010}
}
%  booktitle = {Advances in Neural Information Processing Systems 23: 24th Annual
%               Conference on Neural Information Processing Systems 2010. Proceedings
%               of a meeting held 6-9 December 2010, Vancouver, British Columbia,
%               Canada},
 % pages     = {2424--2432},
 % publisher = {Curran Associates, Inc.},
%  url       = {http://papers.nips.cc/paper/4074-the-multidimensional-wisdom-of-crowds},
%  timestamp = {Fri, 06 Mar 2020 16:57:41 +0100},
%  biburl    = {https://dblp.org/rec/conf/nips/WelinderBBP10.bib},
%  bibsource = {dblp computer science bibliography, https://dblp.org}


@article{learning2010,
  title={Learning from crowds.},
  author={Raykar, Vikas C and Yu, Shipeng and Zhao, Linda H and Valadez, Gerardo Hermosillo and Florin, Charles and Bogoni, Luca and Moy, Linda},
 journal={JMLR},
  year={2010}
}
% journal={Journal of Machine Learning Research},
%  volume={11},
%  number={4},


@article{maximum1979,
  title={Maximum likelihood estimation of observer error-rates using the EM algorithm},
  author={Dawid, Alexander Philip and Skene, Allan M},
  journal={Journal of the Royal Statistical Society},
  year={1979}
}
%  volume={28},
%  number={1},
%  pages={20--28},
% publisher={Wiley Online Library}

@inproceedings{DBLP:conf/atal/KrugerEFQ14,
  author    = {Justin Kruger and
               Ulle Endriss and
               Raquel Fern{\'{a}}ndez and
               Ciyang Qing},
  title     = {Axiomatic analysis of aggregation methods for collective annotation},
  booktitle = {AAMAS},
  year      = {2014}
}
%  editor    = {Ana L. C. Bazzan and
%               Michael N. Huhns and
%               Alessio Lomuscio and
%               Paul Scerri},
%booktitle = {International conference on Autonomous Agents and Multi-Agent Systems,
%               {AAMAS} '14, Paris, France, May 5-9, 2014},
%  pages     = {1185--1192},
%  publisher = {{IFAAMAS/ACM}},
% url       = {http://dl.acm.org/citation.cfm?id=2617437},
%  timestamp = {Fri, 26 Sep 2014 07:51:30 +0200},
%  biburl    = {https://dblp.org/rec/conf/atal/KrugerEFQ14.bib},
%  bibsource = {dblp computer science bibliography, https://dblp.org}

@article{Premises2008, 
title={The Premises of Condorcet's Jury Theorem Are Not Simultaneously Justified}, 
journal={Episteme}, 
author={Dietrich, Franz}, 
year={2008}
}
%volume={5}, 
%DOI={10.3366/E1742360008000233}, 
%number={1}, 
%publisher={Cambridge University Press}, 
%pages={56–73}


%%%%%%%%%%%%
%%%%%%%%%%%%
@book{condorcet1785,
  title={Essai sur l'application de l'analyse à la probabilité des décisions rendues à la pluralité des voix},
  author={Condorcet},
  year={1785}
}
% journal={De l'imprimerie royale, Paris},



@article{condorcet1988,
  title={Condorcet's theory of voting},
  author={Young, H. Peyton},
  journal={American Political science review},
  year={1988}
  }
%  volume={82},
%  number={4},
%  pages={1231--1244},
%  publisher={Cambridge University Press}


@book{optimal1986,
  title={Information pooling and group decision making},
  author={Young, H Peyton},
  year={1986},
}
% publisher={Jai}
%  pages={113--122},

@article{optimal1995,
  title={Optimal voting rules},
  author={Young, Peyton},
  journal={Journal of Economic Perspectives},
  year={1995}
}
%  volume={9},
%  number={1},
%  pages={51--64},

@article{maximum2004,
  title={Maximum likelihood approach to vote aggregation with variable probabilities},
  author={Drissi-Bakhkhat, Mohamed and Truchon, Michel},
  journal={Social Choice and Welfare},
  year={2004}
}
%  volume={23},
%  number={2},
%  pages={161--185},
%  publisher={Springer}

@inproceedings{common2005,
author = {Conitzer, Vincent and Sandholm, Tuomas},
title = {Common Voting Rules as Maximum Likelihood Estimators},
year = {2005},
booktitle = {UAI}
}
%isbn = {0974903914},
%publisher = {AUAI Press},
%address = {Arlington, Virginia, USA},
%abstract = {Voting is a very general method of preference aggregation. A voting rule takes as input every voter's vote (typically, a ranking of the alternatives), and produces as output either just the winning alternative or a ranking of the alternatives. One potential view of voting is the following. There exists a "correct" outcome (winner/ranking), and each voter's vote corresponds to a noisy perception of this correct outcome. If we are given the noise model, then for any vector of votes, we can compute the maximum likelihood estimate of the correct outcome. This maximum likelihood estimate constitutes a voting rule. In this paper, we ask the following question: For which common voting rules does there exist a noise model such that the rule is the maximum likelihood estimate for that noise model? We require that the votes are drawn independently given the correct outcome (we show that without this restriction, all voting rules have the property). We study the question both for the case where outcomes are winners and for the case where outcomes are rankings. In either case, only some of the common voting rules have the property. Moreover, the sets of rules that satisfy the property are incomparable between the two cases (satisfying the property in the one case does not imply satisfying it in the other case).},
%booktitle = {Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence},
%pages = {145–152},
%numpages = {8},
%location = {Edinburgh, Scotland},
%series = {UAI'05}


@inproceedings{maximum2011,
  title={A maximum likelihood approach towards aggregating partial orders},
  author={Xia, Lirong and Conitzer, Vincent},
  booktitle={IJCAI},
  year={2011}
}
%  pages={446--451},

@inproceedings{maximum2012,
  author    = {Ariel D. Procaccia and
               Sashank Jakkam Reddi and
               Nisarg Shah},
  title     = {A Maximum Likelihood Approach For Selecting Sets of Alternatives},
  booktitle = {UAI},
  year      = {2012}
}
%  editor    = {Nando de Freitas and Kevin P. Murphy},
%  url       = {https://dslpitt.org/uai/displayArticleDetails.jsp?mmnu=1\&smnu=2\&article\_id=2333\&proceeding\_id=28},
%  timestamp = {Wed, 29 Mar 2017 16:45:26 +0200},
%  biburl    = {https://dblp.org/rec/conf/uai/ProcacciaRS12.bib},
%  bibsource = {dblp computer science bibliography, https://dblp.org}
%  booktitle = {Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial
%               Intelligence, Catalina Island, CA, USA, August 14-18, 2012},
%  pages     = {695--704},
%  publisher = {{AUAI} Press},

@article{voting2011,
  author    = {Marcus Pivato},
  title     = {Voting rules as statistical estimators},
  journal   = {Social Choice and Welfare},
  year      = {2013}
}
%  volume    = {40},
%  number    = {2},
%  pages     = {581--630},
% url       = {https://doi.org/10.1007/s00355-011-0619-1},
%  doi       = {10.1007/s00355-011-0619-1},
%  timestamp = {Wed, 22 Jul 2020 21:59:19 +0200},
%  biburl    = {https://dblp.org/rec/journals/scw/Pivato13a.bib},
%  bibsource = {dblp computer science bibliography, https://dblp.org}
  
@article{epistemic2017,
title = "Epistemic democracy with correlated voters",
journal = "Journal of Mathematical Economics",
year = "2017",
author = "Marcus Pivato"
}
%keywords = "Condorcet Jury Theorem, Wisdom of Crowds, Epistemic social choice, Deliberation, Social network, DeGroot",
%abstract = "We develop a general theory of epistemic democracy in large societies, which subsumes the classical Condorcet Jury Theorem, the Wisdom of Crowds, and other similar results. We show that a suitably chosen voting rule will converge to the correct answer in the large-population limit, even if there is significant correlation amongst voters, as long as the average covariance between voters becomes small as the population becomes large. Finally, we show that these hypotheses are consistent with models where voters are correlated via a social network, or through the DeGroot model of deliberation."
%volume = "72",
%pages = "51 - 69",
%issn = "0304-4068",
%doi = "https://doi.org/10.1016/j.jmateco.2017.06.001",
%url = "http://www.sciencedirect.com/science/article/pii/S0304406816301094",

@inproceedings{better2013,
  author    = {Andrew Mao and
               Ariel D. Procaccia and
               Yiling Chen},
  title     = {Better Human Computation Through Principled Voting},
  booktitle = {AAAI},
  year      = {2013}
}
%editor    = {Marie desJardins and Michael L. Littman},
% booktitle = {Proceedings of the Twenty-Seventh {AAAI} Conference on Artificial Intelligence, July 14-18, 2013, Bellevue, Washington, {USA}},
%url       = {http://www.aaai.org/ocs/index.php/AAAI/AAAI13/paper/view/6279},
%  timestamp = {Tue, 17 Dec 2013 19:26:12 +0100},
%  biburl    = {https://dblp.org/rec/conf/aaai/MaoPC13.bib},
%  bibsource = {dblp computer science bibliography, https://dblp.org}
%  publisher = {{AAAI} Press},

@inproceedings{isapproval2015,
  author    = {Ariel D. Procaccia and
               Nisarg Shah},
  title     = {Is Approval Voting Optimal Given Approval Votes?},
  booktitle = {NIPS},
  year      = {2015}
}
%  pages     = {1801--1809},

@article{aggregating2020,
  title={Aggregating Binary Judgments Ranked By Accuracy},
  author={Halpern, Daniel and Kehne, Gregory and Peters, Dominik and Procaccia, Ariel D and Shah, Nisarg and Skowron, Piotr}
}

@article{optimal1997,
 author = {Ruth C. Ben-Yashar and Shmuel I. Nitzan},
 journal = {International Economic Review},
 title = {The Optimal Decision Rule for Fixed-Size Committees in Dichotomous Choice Situations: The General Result},
  year = {1997}
}
% volume = {38},
% number = {1},
% pages = {175--186},
% publisher = {[Economics Department of the University of Pennsylvania, Wiley, Institute of Social and Economic Research, Osaka University]},

@article{optimal2001,
  author    = {Ruth Ben{-}Yashar and
               Jacob Paroush},
  title     = {Optimal decision rules for fixed-size committees in polychotomous
               choice situations},
  journal   = {Soc. Choice Welf.},
  volume    = {18},
  number    = {4},
  pages     = {737--746},
  year      = {2001},
  url       = {https://doi.org/10.1007/s003550000080},
  doi       = {10.1007/s003550000080},
  timestamp = {Wed, 22 Jul 2020 21:59:06 +0200},
  biburl    = {https://dblp.org/rec/journals/scw/Ben-YasharP01.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}




@inproceedings{approval2015,
  title={Approval voting and incentives in crowdsourcing},
  author={Shah, Nihar and Zhou, Dengyong and Peres, Yuval},
 booktitle={ICML},
  year={2015}
}
%  booktitle={International conference on machine learning},
%  pages={10--19},

@article{simple2018,
  title={Simple, robust and optimal ranking from pairwise comparisons},
  author={Shah, Nihar B and Wainwright, Martin J},
  journal={JMLR},
  year={2017}
  }
  %  volume={18},
  %number={1},
  %pages={7246--7283},


@article{majority2020,
  author    = {Hanan Shteingart and
               Eran Marom and
               Igor Itkin and
               Gil Shabat and
               Michael Kolomenkin and
               Moshe Salhov and
               Liran Katzir},
  title     = {Majority Voting and the {C}ondorcet's Jury Theorem},
  journal   = {arvix 2002.03153},
  year      = {2020}
}

@inproceedings{Collective2013,
  author    = {Ulle Endriss and
               Raquel Fern{\'{a}}ndez},
  title     = {Collective Annotation of Linguistic Resources: Basic Principles and
               a Formal Model},
  booktitle = {ACL},
  year      = {2013}
}
% pages     = {539--549},
%  publisher = {The Association for Computer Linguistics},

@inproceedings{Empirical2014,
  author    = {Ciyang Qing and
               Ulle Endriss and
               Raquel Fern{\'{a}}ndez and
               Justin Kruger},
  title     = {Empirical Analysis of Aggregation Methods for Collective Annotation},
  booktitle = {COLING},
  year      = {2014}
}
%  pages     = {1533--1542},
%  publisher = {{ACL}},



@inproceedings{Axiomatic2014,
  author    = {Justin Kruger and
               Ulle Endriss and
               Raquel Fern{\'{a}}ndez and
               Ciyang Qing},
  title     = {Axiomatic analysis of aggregation methods for collective annotation},
  booktitle = {AAMAS},
  year      = {2014}
}
%  pages     = {1185--1192},


@inproceedings{Electing2014,
  author    = {Edith Elkind and
               Nisarg Shah},
  editor    = {Nevin L. Zhang and
               Jin Tian},
  title     = {Electing the Most Probable Without Eliminating the Irrational: Voting
               Over Intransitive Domains},
  booktitle = {UAI},
  year      = {2014}
}
%  pages     = {182--191},


@inproceedings{learning2017,
  author    = {Ioannis Caragiannis and
               Evi Micha},
  title     = {Learning a Ground Truth Ranking Using Noisy Approval Votes},
  booktitle = {IJCAI},
  year      = {2017}
}
%  pages     = {149--155},


@inproceedings{optimal2019,
  author    = {Zoi Terzopoulou and
               Ulle Endriss},
  title     = {Optimal Truth-Tracking Rules for the Aggregation of Incomplete Judgments},
  booktitle = {SAGT},
  year      = {2019}
}
%  volume    = {11801},
%  pages     = {298--311},
%  publisher = {Springer},

@inproceedings{Evaluating2020,
  author    = {Ioannis Caragiannis and
               Christos Kaklamanis and
               Nikos Karanikolas and
               George A. Krimpas},
  title     = {Evaluating Approval-Based Multiwinner Voting in Terms of Robustness
               to Noise},
  booktitle = {IJCAI},
  year      = {2020}
}
%pages     = {74--80},


%%%%%%%%%%
%%%%%%%%%%
@inproceedings{multilabel2018,
  title={Multi-label inference for crowdsourcing},
  author={Zhang, Jing and Wu, Xindong},
  booktitle={ICKDM},
  year={2018}
}
% booktitle={ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
%  pages={2738--2747},


@article{online2009,
  title={On-line expectation--maximization algorithm for latent data models},
  author={Capp{\'e}, Olivier and Moulines, Eric},
  journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume={71},
  number={3},
  pages={593--613},
  year={2009},
  publisher={Wiley Online Library}
}


@inproceedings{reliable2010,
  author    = {Stefanie Nowak and
               Stefan M. R{\"{u}}ger},
  title     = {How reliable are annotations via crowdsourcing: a study about inter-annotator
               agreement for multi-label image annotation},
  booktitle = {MIR},
  year      = {2010}
}
%  pages     = {557--566},
%  publisher = {{ACM}},

@inproceedings{scalable2014,
  author    = {Jia Deng and
               Olga Russakovsky and
               Jonathan Krause and
               Michael S. Bernstein and
               Alexander C. Berg and
               Fei{-}Fei Li},
  title     = {Scalable multi-label annotation},
  booktitle = {{CHI} Conference on Human Factors in Computing Systems},
  year      = {2014}
}
%  pages     = {3099--3102},
%  publisher = {{ACM}},

@article{computing2018,
  author    = {Nguyen Quoc Viet Hung and
               Huynh Huu Viet and
               Thanh Tam Nguyen and
               Matthias Weidlich and
               Hongzhi Yin and
               Xiaofang Zhou},
  title     = {Computing Crowd Consensus with Partial Agreement},
  journal   = {IEEE Transaction on Knowledge and Data Engineering},
  year      = {2018}
}
%volume    = {30},
%  number    = {1},
%  pages     = {1--14},

%%%%%%%%%%%
%%%%%%%%%%%

@inproceedings{multilabel2011,
  author    = {Chuan Shi and
               Xiangnan Kong and
               Philip S. Yu and
               Bai Wang},
  title     = {Multi-label Ensemble Learning},
  booktitle = {ECML-PKDD},
  year      = {2011}
}
% volume    = {6913},
%  pages     = {223--239},

@inproceedings{aggregation2020,
  author    = {Vu{-}Linh Nguyen and
               Eyke H{\"{u}}llermeier and
               Michael Rapp and
               Eneldo Loza Menc{\'{\i}}a and
               Johannes F{\"{u}}rnkranz},
  title     = {On Aggregation in Ensembles of Multilabel Classifiers},
  booktitle = {Discovery Science},
  year      = {2020}
}
%  volume    = {12323},
%  pages     = {533--547},


@inproceedings{AlloucheLY22,
  author    = {Tahar Allouche and J{\'e}r{\^o}me Lang and Florian Yger},
  title     = {Truth-tracking via Approval Voting: Size Matters},
  booktitle = {AAAI},
  year      = {2022}
}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@article{kemeny1959,
 author = {John G. Kemeny},
 journal = {Daedalus},
 title = {Mathematics without Numbers},
 year = {1959}
}
% number = {4},
% pages = {577--591},
% publisher = {The MIT Press},
% volume = {88},


@article{borda1781,
  title={Mémoire sur les élections au scrutin},
  author={Borda, Jean-Charles },
  journal={Histoire de l'Académie royale des sciences, Paris},
  year={1781}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@article{CrowdData,
  title={Crowdsourcing for video event detection},
  author={Iepsma, Robert and Gevers, Theo and Szlavik, Zoltan and Aroyo, Lora},
  year={2016},
  journal={Master Thesis}
}

@article{dempster1977maximum,
  title={Maximum likelihood from incomplete data via the EM algorithm},
  author={Dempster, Arthur P and Laird, Nan M and Rubin, Donald B},
  journal={Journal of the Royal Statistical Society},
  year={1977}
  }
%  volume={39},
%  number={1},
%  pages={1--22},
%  publisher={Wiley Online Library}


@incollection{ElkindSlinko16,
  author    = {Edith Elkind and
               Arkadii Slinko},
  title     = {Rationalizations of Voting Rules},
  booktitle = {Handbook of Computational Social Choice},
  year      = {2016}
}
%  pages     = {169--196},


@article{ShapleyGrofman84,
title = {Optimizing group judgmental accuracy in the presence of interdependencies},
author = {Lloyd Shapley and Bernard  Grofman},
journal = {Public Choice},
year = {1984}
}
%volume = {43},
%number = {3}, 
%pages = {329-343},

@article{Ben-YasharN97,
 author = {Ruth C. Ben-Yashar and Shmuel I. Nitzan},
 journal = {International Economic Review},
 title = {The Optimal Decision Rule for Fixed-Size Committees in Dichotomous Choice Situations: The General Result},
 year = {1997}
}
% volume = {38},
%number = {1},
% pages = {175--186},
% publisher = {[Economics Department of the University of Pennsylvania, Wiley, Institute of Social and Economic Research, Osaka University]},

@inproceedings{ConitzerRX09,
  author    = {Vincent Conitzer and
               Matthew Rognlie and
               Lirong Xia},
  title     = {Preference Functions that Score Rankings and Maximum Likelihood Estimation},
  booktitle = {IJCAI},
  year      = {2009}
}
%  pages     = {109--115},


@inproceedings{XiaCL10,
  author    = {Lirong Xia and
               Vincent Conitzer and
               J{\'{e}}r{\^{o}}me Lang},
  title     = {Aggregating preferences in multi-issue domains by using maximum likelihood estimators},
  booktitle = {AAMAS},
  year      = {2010}
}
%  pages     = {399--408},


@incollection{FaliszewskiSST17,
title = {Multiwinner voting: A new challenge for social choice theory},
author = {Piotr Faliszewski and Piotr Skowron and Arkadii Slinko and Nimrod Talmon},
booktitle = {Trends in {C}omputational {S}ocial {C}hoice},
year = {2017}
}
%pages = {27-47},
%publisher = {AI Access},


@article{ShahZ20,
  author    = {Nihar B. Shah and
               Dengyong Zhou},
  title     = {Approval Voting and Incentives in Crowdsourcing},
  journal   = {{ACM} Transactions on Economics and Computation},
  year      = {2020}
}
%  volume    = {8},
%  number    = {3},
%  pages     = {13:1--13:40},

