Research map / Learning theory and algorithms / Prediction and decision losses
Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use low-rank nuclear-norm-constrained quantile recovery with a small number of manager ratings | Adversarial crowdsourcing · Crowdsourcing model, n = m | average quality loss: at most ϵ worse than T* | Helped |
| Use Algorithms 1–3 under independent reliable-worker errors and monotonic ratings | Adversarial crowdsourcing · Crowdsourcing model, m ≥ n | average quality loss: (2L + 1) · ϵ + 2ϵ0 | Helped |
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- JAG: A Crowdsourcing Framework for Joint Assessment and Peer Grading (AAAI 2017 · similarity 0.86)
- HirePeer: Impartial Peer-Assessed Hiring at Scale in Expert Crowdsourcing Markets (AAAI 2020 · similarity 0.85)
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