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The task of aggregating and denoising crowd-labeled data has gained increased significance with the advent of crowdsourcing platforms and massive datasets.
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S. Lazebnik, C. Schmid, and J. Ponce · 2005
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V. S. Sheng, F. Provost, and P. G. Ipeirotis · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Whose vote should count more: Optimal integration of labels from labelers of unknown expertise
J. Whitehill, T.-f. Wu, J. Bergsma, J. R. Movellan, and P. L. Ruvolo · 2009
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How crowdsourcable is your task
C. Eickhoff and A. de Vries · 2011
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Who moderates the moderators?: Crowdsourcing abuse detection in user-generated content
A. Ghosh, S. Kale, and P. McAfee · 2011
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Budget-optimal crowdsourcing using low-rank matrix approximations
D. Karger, S. Oh, and D. Shah · 2011
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Iterative learning for reliable crowdsourcing systems
D. Karger, S. Oh, and D. Shah · 2011
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Novel dataset for fine-grained image categorization
A. Khosla, N. Jayadevaprakash, B. Yao, and L. Fei-Fei · 2011
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A survey of crowdsourcing systems
M.-C. Yuen, I. King, and K.-S. Leung · 2011
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Elements of information theory
T. M. Cover and J. A. Thomas · 2012
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Variational inference for crowdsourcing
Q. Liu, J. Peng, and A. T. Ihler · 2012
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Learning from the wisdom of crowds by minimax entropy
D. Zhou, J. Platt, S. Basu, and Y. Mao · 2012
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Spectral methods meet em: A provably optimal algorithm for crowdsourcing
Y. Zhang, X. Chen, D. Zhou, and M. I. Jordan · 2016
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Stochastically transitive models for pairwise comparisons: Statistical and computational issues
N. B. Shah, S. Balakrishnan, A. Guntuboyina, and M. J. Wainwright · 2017
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Breaking the 1/sqrt n barrier: Faster rates for permutation-based models in polynomial time
C. Mao, A. Pananjady, and M. J. Wainwright · 2018
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Simple, robust and optimal ranking from pairwise comparisons
N. B. Shah and M. J. Wainwright · 2018
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Optimal rates of statistical seriation
N. Flammarion, C. Mao, and P. Rigollet · 2019
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Global empirical risk minimizers with” shape constraints” are rate optimal in general dimensions
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Aggregating crowdsourced binary ratings
N. Dalvi, A. Dasgupta, R. Kumar, and V. Rastogi · 2013
Cited alongside, same era.
Minimax optimal convergence rates for estimating ground truth from crowdsourced labels
C. Gao and D. Zhou · 2013
Cited alongside, same era.
Ranking and combining multiple predictors without labeled data
F. Parisi, F. Strino, B. Nadler, and Y. Kluger · 2014
Cited alongside, same era.
Training workers for improving performance in crowdsourcing microtasks
U. Gadiraju, B. Fetahu, and R. Kawase · 2015
Cited alongside, same era.
Understanding malicious behavior in crowdsourcing platforms: The case of online surveys
U. Gadiraju, R. Kawase, S. Dietze, and G. Demartini · 2015
Cited alongside, same era.
Estimating the accuracies of multiple classifiers without labeled data
A. Jaffe, B. Nadler, and Y. Kluger · 2015
Cited alongside, same era.
Q. Han · 2019
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Active ranking from pairwise comparisons and when parametric assumptions do not help
R. Heckel, N. B. Shah, K. Ramchandran, M. J. Wainwright, et al · 2019
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Feeling the Bern: Adaptive estimators for bernoulli probabilities of pairwise comparisons
N. B. Shah, S. Balakrishnan, and M. J. Wainwright · 2019
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Low permutation-rank matrices: Structural properties and noisy completion
N. B. Shah, S. Balakrishnan, and M. J. Wainwright · 2019
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On testing for biases in peer review
I. Stelmakh, N. Shah, and A. Singh · 2019
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PeerReview4All: Fair and accurate reviewer assignment in peer review
I. Stelmakh, N. Shah, and A. Singh · 2019
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Your 2 is my 1, your 3 is my 9: Handling arbitrary miscalibrations in ratings
J. Wang and N. B. Shah · 2019
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A nonasymptotic law of iterated logarithm for general m-estimators
A. Dalalyan, N. Schreuder, and V.-E. Brunel · 2020
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Better algorithms for estimating non-parametric models in crowd-sourcing and rank aggregation
A. Liu and A. Moitra · 2020
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Loss functions, axioms, and peer review
R. Noothigattu, N. Shah, and A. Procaccia · 2020
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Debiasing evaluations that are biased by evaluations
J. Wang, I. Stelmakh, Y. Wei, and N. Shah · 2021
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