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Simple, robust and optimal ranking from pairwise comparisons
We consider data in the form of pairwise comparisons of n items, with the goal of identifying
the top k items for some value of k< n, or alternatively, recovering a ranking of all the items …
the top k items for some value of k< n, or alternatively, recovering a ranking of all the items …
Exploiting worker correlation for label aggregation in crowdsourcing
Crowdsourcing has emerged as a core component of data science pipelines. From collected
noisy worker labels, aggregation models that incorporate worker reliability parameters aim …
noisy worker labels, aggregation models that incorporate worker reliability parameters aim …
Your 2 is my 1, your 3 is my 9: Handling arbitrary miscalibrations in ratings
Cardinal scores (numeric ratings) collected from people are well known to suffer from
miscalibrations. A popular approach to address this issue is to assume simplistic models of …
miscalibrations. A popular approach to address this issue is to assume simplistic models of …
Double or nothing: Multiplicative incentive mechanisms for crowdsourcing
Crowdsourcing has gained immense popularity in machine learning applications for
obtaining large amounts of labeled data. Crowdsourcing is cheap and fast, but suffers from …
obtaining large amounts of labeled data. Crowdsourcing is cheap and fast, but suffers from …
Learning from crowds by modeling common confusions
Crowdsourcing provides a practical way to obtain large amounts of labeled data at a low
cost. However, the annotation quality of annotators varies considerably, which imposes new …
cost. However, the annotation quality of annotators varies considerably, which imposes new …
Achieving budget-optimality with adaptive schemes in crowdsourcing
Adaptive schemes, where tasks are assigned based on the data collected thus far, are
widely used in practical crowdsourcing systems to efficiently allocate the budget. However …
widely used in practical crowdsourcing systems to efficiently allocate the budget. However …
Max-mig: an information theoretic approach for joint learning from crowds
Eliciting labels from crowds is a potential way to obtain large labeled data. Despite a variety
of methods developed for learning from crowds, a key challenge remains unsolved:\emph …
of methods developed for learning from crowds, a key challenge remains unsolved:\emph …
Isotonic regression with unknown permutations: Statistics, computation and adaptation
A Pananjady, RJ Samworth - The Annals of Statistics, 2022 - projecteuclid.org
Isotonic regression with unknown permutations: Statistics, computation and adaptation Page 1
The Annals of Statistics 2022, Vol. 50, No. 1, 324–350 https://doi.org/10.1214/21-AOS2107 © …
The Annals of Statistics 2022, Vol. 50, No. 1, 324–350 https://doi.org/10.1214/21-AOS2107 © …
Approval voting and incentives in crowdsourcing
The growing need for labeled training data has made crowdsourcing a vital tool for
develo** machine learning applications. Here, workers on a crowdsourcing platform are …
develo** machine learning applications. Here, workers on a crowdsourcing platform are …
Adversarial crowdsourcing through robust rank-one matrix completion
We consider the problem of reconstructing a rank-one matrix from a revealed subset of its
entries when some of the revealed entries are corrupted with perturbations that are unknown …
entries when some of the revealed entries are corrupted with perturbations that are unknown …