How Much Spam Can You Take? An Analysis of Crowdsourcing Results to Increase Accuracy

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Certified Defenses for Data Poisoning Attacks (2017, methodology): “…(Xiao et al., 2015a), clustering (Biggio et al., 2013; 2014c), PCA (Rubinstein et al., 2009), topic modeling (Mei and Zhu, 2015a), collaborative ﬁltering (Li et al., 2016), neural networks (Yang et al., 2017), and other models (Mozaffari-Kermani et al., 2015; Vuurens et al., 2011; Wang, 2016).”
Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey (2019, background): “sourced data [141]–[143] or datasets which requires high level of expertise such as medical imaging [18].”
Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction (2016, background): “Versions of this question arise in crowdsourcing (Vuurens et al., 2011), collaborative knowledge generation (Priedhorsky et al., 2007), peer grading in online classrooms (Piech et al., 2013; Kulkarni et al., 2015), aggregation of customer reviews (Harmon, 2004), and the generation/curation of large…”
Rewarding High-Quality Data via Influence Functions (2019, background): “This phenomenon has been documented in practice (e.g., see [ Vuurens et al. , 2011; Shah et al. , 2015 ] and references therein) and is justiﬁed by the principles of agent rationality, an integral part of the ﬁeld of game theory .”