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Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers

ICML 2018 · Learning theory and algorithms › Matrix and tensor methods · approach: Nonconvex low-rank and sparse optimization · 11 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use weighted rank-one PGD skill estimation instead of majority votingweighted-majority label aggregation · Webprediction error: 0.2693 -> 0.1485Helped
use PGD instead of majority votinglabel aggregation · RTE1prediction error: 0.1031 -> 0.07Helped
use PGD instead of Opt-D&Slabel aggregation · Dogsprediction error: 0.1689 -> 0.1660Helped
use PGD instead of ERlabel aggregation · Tempprediction error: 0.052 -> 0.0512Helped
use PGD instead of KOSlabel aggregation · RTE1prediction error: 0.3975 -> 0.07Helped
use PGD instead of Opt-D&S on sparse ring graphlabel aggregation · Synthetic ring graphprediction errorHelped
increase number of tasksPGD · Synthetic graphs (clique, star, ring)average prediction errorHelped
sparsify worker interaction graph (lower maximum degree)PGD · RTE1 and Tempaverage prediction errorHurt

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