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Crowdsourcing has gained immense popularity in machine learning applications for obtaining large amounts of labeled data.
Verification of forecasts expressed in terms of probability
Glenn W Brier · 1950
Earlier work this paper cites.
The magical number seven, plus or minus two: some limits on our capacity for processing information
George A Miller · 1956
Earlier work this paper cites.
Elicitation of personal probabilities and expectations
Leonard J Savage · 1971
Earlier work this paper cites.
Maximum likelihood estimation of observer error-rates using the EM algorithm
Alexander Philip Dawid and Allan M Skene · 1979
Earlier work this paper cites.
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Dana Angluin and Philip Laird · 1988
Earlier work this paper cites.
Seven plus or minus two: A commentary on capacity limitations
Richard M Shiffrin and Robert M Nosofsky · 1994
Earlier work this paper cites.
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Izquierdo JM Cano, Yannis A Dimitriadis, Sánchez E Gómez, and Coronado J López · 2001
Earlier work this paper cites.
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A hybrid neural network model for noisy data regression
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Loss functions for binary class probability estimation and classification: Structure and applications
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Tilmann Gneiting and Adrian E Raftery · 2007
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Boosted classification trees and class probability/quantile estimation
David Mease, Abraham J Wyner, and Andreas Buja · 2007
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Incentive compatible regression learning
Ofer Dekel, Felix Fischer, and Ariel D Procaccia · 2008
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reCAPTCHA: Human-based character recognition via web security measures
Luis Von Ahn, Benjamin Maurer, Colin McMillen, David Abraham, and Manuel Blum · 2008
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How well does active learning actually work?: Time-based evaluation of cost-reduction strategies for language documentation
Jason Baldridge and Alexis Palmer · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Eliciting truthful answers to multiple-choice questions
Nicolas Lambert and Yoav Shoham · 2009
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Soylent: a word processor with a crowd inside
Michael S Bernstein, Greg Little, Robert C Miller, Björn Hartmann, Mark S Ackerman, David R Karger, David Crowell, and Katrina Panovich · 2010
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Coupled semi-supervised learning for information extraction
Andrew Carlson, Justin Betteridge, Richard C Wang, Estevam R Hruschka Jr, and Tom M Mitchell · 2010
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Negative results for active learning with convex losses
Steve Hanneke and Liu Yang · 2010
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Ensuring quality in crowdsourced search relevance evaluation: The effects of training question distribution
John Le, Andy Edmonds, Vaughn Hester, and Lukas Biewald · 2010
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Random classification noise defeats all convex potential boosters
Task matching in crowdsourcing
Man-Ching Yuen, Irwin King, and Kwong-Sak Leung · 2011
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al · 2012
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Combining human and machine intelligence in large-scale crowdsourcing
Ece Kamar, Severin Hacker, and Eric Horvitz · 2012
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Variational inference for crowdsourcing
Qiang Liu, Jian Peng, and Alexander T Ihler · 2012
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Learning from the wisdom of crowds by minimax entropy
Dengyong Zhou, John Platt, Sumit Basu, and Yi Mao · 2012
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Pairwise ranking aggregation in a crowdsourced setting
Xi Chen, Paul N Bennett, Kevyn Collins-Thompson, and Eric Horvitz · 2013
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Philip M Long and Rocco A Servedio · 2010
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Learning from crowds
Vikas C Raykar, Shipeng Yu, Linda H Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, and Linda Moy · 2010
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Towards building a high-quality workforce with Mechanical Turk
Paul Wais, Shivaram Lingamneni, Duncan Cook, Jason Fennell, Benjamin Goldenberg, Daniel Lubarov, David Marin, and Hari Simons · 2010
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Social science for pennies
John Bohannon · 2011
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Opportunities for crowdsourcing research on amazon mechanical turk
Jenny J Chen, Natala J Menezes, Adam D Bradley, and TA North · 2011
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CrowdDB: answering queries with crowdsourcing
Michael J Franklin, Donald Kossmann, Tim Kraska, Sukriti Ramesh, and Reynold Xin · 2011
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Crystal structure of a monomeric retroviral protease solved by protein folding game players
Firas Khatib, Frank DiMaio, Seth Cooper, Maciej Kazmierczyk, Miroslaw Gilski, Szymon Krzywda, Helena Zabranska, Iva Pichova, James Thompson, Zoran Popović, Mariusz Jaskolski, and David Baker · 2011
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Adaptive task assignment for crowdsourced classification
Chien-Ju Ho, Shahin Jabbari, and Jennifer W Vaughan · 2013
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Optimal number of questionnaire response categories more may not be better
W Paul Jones and Scott A Loe · 2013
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Noise tolerance under risk minimization
Naresh Manwani and PS Sastry · 2013
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Mechanism design for crowdsourcing: An optimal 1-1/e competitive budget-feasible mechanism for large markets
Nima Anari, Gagan Goel, and Afshin Nikzad · 2014
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http://wikipedia.org/wiki/Double_or_nothing , 2014
Double or Nothing · 2014
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Repeated labeling using multiple noisy labelers
Panagiotis G Ipeirotis, Foster Provost, Victor S Sheng, and Jing Wang · 2014
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Reputation-based worker filtering in crowdsourcing
Srikanth Jagabathula, Lakshminarayanan Subramanian, and Ashwin Venkataraman · 2014
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Reliable crowdsourcing for multi-class labeling using coding theory
Aditya Vempaty, Lav R Varshney, and Pramod K Varshney · 2014
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Optimal PAC multiple arm identification with applications to crowdsourcing
Yuan Zhou, Xi Chen, and Jian Li · 2014
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Spectral methods meet EM: A provably optimal algorithm for crowdsourcing
Yuchen Zhang, Xi Chen, Dengyong Zhou, and Michael I Jordan · 2014
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Optimum statistical estimation with strategic data sources
Yang Cai, Constantinos Daskalakis, and Christos H Papadimitriou · 2015
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Estimation from pairwise comparisons: Sharp minimax bounds with topology dependence
Nihar B Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramchandran, and Martin J Wainwright · 2015
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