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Efficient crowdsourcing for multi-class labeling

https://doi.org/10.1145/2465529.2465761
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/rsa.20089openalex-snapshot
Spectral techniques applied to sparse random graphs
inferredcites →https://doi.org/10.1002/rsa.v27:2openalex-snapshot
inferredcites →https://doi.org/10.1007/bf02579208openalex-snapshot
Sharp concentration of the chromatic number on random graphsG n, p
inferredcites →https://doi.org/10.1017/cbo9780511791338openalex-snapshot
Modern Coding Theory
inferredcites →https://doi.org/10.1017/cbo9780511814068openalex-snapshot
Random Graphs
inferredcites →https://doi.org/10.1109/allerton.2011.6120180openalex-snapshot
Budget-optimal crowdsourcing using low-rank matrix approximations
inferredcites →https://doi.org/10.1109/isit.2009.5205567openalex-snapshot
Matrix completion from a few entries
inferredcites →https://doi.org/10.1109/tit.2010.2046205openalex-snapshot
Matrix Completion From a Few Entries
inferredcites →https://doi.org/10.1111/j.2517-6161.1977.tb01600.xopenalex-snapshot
Maximum Likelihood from Incomplete Data Via the EM Algorithm
inferredcites →https://doi.org/10.1145/1401890.1401965openalex-snapshot
Get another label? improving data quality and data mining using multiple, noisy labelers
inferredcites →https://doi.org/10.1145/1837885.1837906openalex-snapshot
Quality management on Amazon Mechanical Turk
inferredcites →https://doi.org/10.1145/1866029.1866078openalex-snapshot
Soylent
inferredcites →https://doi.org/10.1145/1993574.1993599openalex-snapshot
Who moderates the moderators?
inferredcites →https://doi.org/10.1145/2047196.2047201openalex-snapshot
Crowds in two seconds
inferredcites →https://doi.org/10.1145/73007.73063openalex-snapshot
On the second eigenvalue of random regular graphs
inferredcites →https://doi.org/10.1287/opre.2013.1235openalex-snapshot
Budget-Optimal Task Allocation for Reliable Crowdsourcing Systems
inferredcites →https://doi.org/10.2307/2346806openalex-snapshot
Maximum Likelihood Estimation of Observer Error-Rates Using the EM Algorithm
inferredcites →https://doi.org/10.5555/1756006.1859894openalex-snapshot
Learning From Crowds
inferredcites →https://doi.org/10.7551/mitpress/1120.001.0001openalex-snapshot
Advances in Neural Information Processing Systems 14

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.2408163openalex-extracted
Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling
inferred← citeshttps://doi.org/10.2139/ssrn.3989656openalex-extracted
Learning True Labels from Noisy Crowd-Sourced Annotations
inferred← citeshttps://doi.org/10.1002/widm.70037openalex-snapshot
A Systematic Literature Survey of Crowdsourcing: Current Status and Future Perspectives
inferred← citeshttps://doi.org/10.1007/978-3-030-30146-0_2openalex-snapshot
A Dynamic Difficulty-Sensitive Worker Distribution Model for Crowdsourcing Quality Management
inferred← citeshttps://doi.org/10.1007/978-3-030-47436-2_20openalex-snapshot
Reliable Aggregation Method for Vector Regression Tasks in Crowdsourcing
inferred← citeshttps://doi.org/10.1007/978-3-030-94676-0_24openalex-snapshot
Eliciting Social Knowledge for Creditworthiness Assessment
inferred← citeshttps://doi.org/10.1007/978-3-319-45817-5_18openalex-snapshot
Modeling for Noisy Labels of Crowd Workers
inferred← citeshttps://doi.org/10.1007/s11235-017-0339-7openalex-snapshot
Budget-constraint mechanism for incremental multi-labeling crowdsensing
inferred← citeshttps://doi.org/10.1007/s11831-021-09577-8openalex-snapshot
Crowdsourcing: Descriptive Study on Algorithms and Frameworks for Prediction
inferred← citeshttps://doi.org/10.1016/j.artint.2020.103351openalex-snapshot
A technical survey on statistical modelling and design methods for crowdsourcing quality control
inferred← citeshttps://doi.org/10.1016/j.eswa.2018.08.032openalex-snapshot
Regression with re-labeling for noisy data
inferred← citeshttps://doi.org/10.1016/j.inffus.2026.104364openalex-snapshot
Self-paced learning for crowdsourcing
inferred← citeshttps://doi.org/10.1016/j.ins.2020.10.010openalex-snapshot
Privacy-preserving and verifiable online crowdsourcing with worker updates
inferred← citeshttps://doi.org/10.1088/1742-6596/1036/1/012015openalex-snapshot
Fast Randomized Semi-Supervised Clustering
inferred← citeshttps://doi.org/10.1093/iwcomp/iwaa013openalex-snapshot
Weighted Voting-Based Effective Free-Viewing Attention Prediction On Web Image Elements
inferred← citeshttps://doi.org/10.1109/access.2019.2896912openalex-snapshot
Crowdsourcing Incentives for Multi-Hop Urban Parcel Delivery Network
inferred← citeshttps://doi.org/10.1109/allerton.2014.7028448openalex-snapshot
Incentive mechanism and protocol design for crowdsourcing systems
inferred← citeshttps://doi.org/10.1109/cvpr.2018.00287openalex-snapshot
Lean Multiclass Crowdsourcing
inferred← citeshttps://doi.org/10.1109/eusipco.2016.7760604openalex-snapshot
Classification of multiple annotator data using variational Gaussian process inference
inferred← citeshttps://doi.org/10.1109/fie61694.2024.10893321openalex-snapshot
Credibility Metrics for Student-Assigned Labels of Textual Comments
inferred← citeshttps://doi.org/10.1109/glocom.2014.7036819openalex-snapshot
Multi-class labeling with BCH codes for mobile crowdsensing
inferred← citeshttps://doi.org/10.1109/glocom.2014.7037221openalex-snapshot
Answer inference for crowdsourcing based scoring
inferred← citeshttps://doi.org/10.1109/icde.2015.7113284openalex-snapshot
Comprehensive and reliable crowd assessment algorithms
inferred← citeshttps://doi.org/10.1109/icdm51629.2021.00174openalex-snapshot
Detecting adversaries in Crowdsourcing
inferred← citeshttps://doi.org/10.1109/icmla.2016.0127openalex-snapshot
Bee Colony Based Worker Reliability Estimation Algorithm in Microtask Crowdsourcing
inferred← citeshttps://doi.org/10.1109/icws49710.2020.00013openalex-snapshot
Answer Aggregation for Crowdsourcing Microtasks using Approximate Global Optimal Searching
inferred← citeshttps://doi.org/10.1109/infocom.2015.7218674openalex-snapshot
Incentivize crowd labeling under budget constraint
inferred← citeshttps://doi.org/10.1109/infocom.2015.7218675openalex-snapshot
The importance of being earnest in crowdsourcing systems
inferred← citeshttps://doi.org/10.1109/infocom48880.2022.9796960openalex-snapshot
Learning for Crowdsourcing: Online Dispatch for Video Analytics with Guarantee
inferred← citeshttps://doi.org/10.1109/isit.2017.8006912openalex-snapshot
Budget-optimal clustering via crowdsourcing
inferred← citeshttps://doi.org/10.1109/ita.2014.6804213openalex-snapshot
Assuring privacy and reliability in crowdsourcing with coding
inferred← citeshttps://doi.org/10.1109/jiot.2019.2930659openalex-snapshot
When Crowdsourcing Meets Social IoT: An Efficient Privacy-Preserving Incentive Mechanism
inferred← citeshttps://doi.org/10.1109/jsac.2017.2680838openalex-snapshot
Incentivize Multi-Class Crowd Labeling Under Budget Constraint
inferred← citeshttps://doi.org/10.1109/jsait.2020.3041804openalex-snapshot
Global Multiclass Classification and Dataset Construction via Heterogeneous Local Experts
inferred← citeshttps://doi.org/10.1109/jstsp.2014.2316116openalex-snapshot
Reliable Crowdsourcing for Multi-Class Labeling Using Coding Theory
inferred← citeshttps://doi.org/10.1109/lcn.2017.74openalex-snapshot
Reliable Machine Learning for Networking: Key Issues and Approaches
inferred← citeshttps://doi.org/10.1109/mass.2015.40openalex-snapshot
Quality-Aware Online Task Assignment in Mobile Crowdsourcing
inferred← citeshttps://doi.org/10.1109/mlsp.2016.7738909openalex-snapshot
Variational Gaussian process for missing label crowdsourcing classification problems
inferred← citeshttps://doi.org/10.1109/sahcn.2019.8824861openalex-snapshot
Online Data Quality Learning for Quality-Aware Crowdsensing
inferred← citeshttps://doi.org/10.1109/sam60225.2024.10636383openalex-snapshot
Labeling Sequential Data From Noisy Annotations
inferred← citeshttps://doi.org/10.1109/tcss.2020.2998732openalex-snapshot
Optimal Spot-Checking for Improving the Evaluation Quality of Crowdsourcing: Application to Peer Grading Systems
inferred← citeshttps://doi.org/10.1109/tit.2015.2499247openalex-snapshot
Matrix Completion With Column Manipulation: Near-Optimal Sample-Robustness-Rank Tradeoffs
inferred← citeshttps://doi.org/10.1109/tit.2018.2846582openalex-snapshot
Optimal Inference in Crowdsourced Classification via Belief Propagation
inferred← citeshttps://doi.org/10.1109/tit.2023.3315720openalex-snapshot
A Worker-Task Specialization Model for Crowdsourcing: Efficient Inference and Fundamental Limits
inferred← citeshttps://doi.org/10.1109/tkde.2014.2327026openalex-snapshot
Multicategory Crowdsourcing Accounting for Variable Task Difficulty, Worker Skill, and Worker Intention
inferred← citeshttps://doi.org/10.1109/tkde.2015.2504974openalex-snapshot
Multi-Class Ground Truth Inference in Crowdsourcing with Clustering
inferred← citeshttps://doi.org/10.1109/tkde.2016.2518669openalex-snapshot
Challenges in Data Crowdsourcing
inferred← citeshttps://doi.org/10.1109/tkde.2017.2750683openalex-snapshot
Computing Crowd Consensus with Partial Agreement
inferred← citeshttps://doi.org/10.1109/tkde.2018.2874003openalex-snapshot
Sequential Multi-Class Labeling in Crowdsourcing
inferred← citeshttps://doi.org/10.1109/tkde.2020.3046645openalex-snapshot
Unsupervised Ensemble Classification With Sequential and Networked Data
inferred← citeshttps://doi.org/10.1109/tkde.2022.3201955openalex-snapshot
Robust Sparse Weighted Classification For Crowdsourcing
inferred← citeshttps://doi.org/10.1109/tmc.2015.2508814openalex-snapshot
CARM: Crowd-Sensing Accurate Outdoor RSS Maps with Error-Prone Smartphone Measurements
inferred← citeshttps://doi.org/10.1109/tmc.2018.2829506openalex-snapshot
Truthful Incentive Mechanism for Nondeterministic Crowdsensing with Vehicles
inferred← citeshttps://doi.org/10.1109/tmc.2023.3269807openalex-snapshot
Crowdsourcing Upon Learning: Energy-Aware Dispatch With Guarantee for Video Analytics
inferred← citeshttps://doi.org/10.1109/tnet.2017.2680245openalex-snapshot
An Online Learning Approach to Improving the Quality of Crowd-Sourcing
inferred← citeshttps://doi.org/10.1109/tpami.2017.2682082openalex-snapshot
Collaborative Active Visual Recognition from Crowds: A Distributed Ensemble Approach
inferred← citeshttps://doi.org/10.1109/tpds.2016.2572078openalex-snapshot
The Importance of Worker Reputation Information in Microtask-Based Crowd Work Systems
inferred← citeshttps://doi.org/10.1109/tsc.2016.2539954openalex-snapshot
Incentive Mechanism and Rating System Design for Crowdsourcing Systems: Analysis, Tradeoffs and Inference
inferred← citeshttps://doi.org/10.1109/tsc.2020.3020338openalex-snapshot
Task Recommendation in Crowdsourcing Based on Learning Preferences and Reliabilities
inferred← citeshttps://doi.org/10.1109/tvt.2014.2363842openalex-snapshot
Quality-Driven Auction-Based Incentive Mechanism for Mobile Crowd Sensing
inferred← citeshttps://doi.org/10.1137/1.9781611974348.60openalex-snapshot
Distributed Representations of Expertise
inferred← citeshttps://doi.org/10.1145/2745844.2745871openalex-snapshot
Reliable Multiple-choice Iterative Algorithm for Crowdsourcing Systems
inferred← citeshttps://doi.org/10.1145/2745844.2745874openalex-snapshot
An Online Learning Approach to Improving the Quality of Crowd-Sourcing
inferred← citeshttps://doi.org/10.1145/2796314.2745871openalex-snapshot
Reliable Multiple-choice Iterative Algorithm for Crowdsourcing Systems
inferred← citeshttps://doi.org/10.1145/2882903.2882953openalex-snapshot
Towards Globally Optimal Crowdsourcing Quality Management
inferred← citeshttps://doi.org/10.1145/2897510openalex-snapshot
Design and Analysis of Incentive and Reputation Mechanisms for Online Crowdsourcing Systems
inferred← citeshttps://doi.org/10.1145/3106426.3106446openalex-snapshot
Sequential multi-class labeling in crowdsourcing
inferred← citeshttps://doi.org/10.1145/3132847.3132848openalex-snapshot
DeMalC
inferred← citeshttps://doi.org/10.1145/3157736openalex-snapshot
Selecting the Top-Quality Item Through Crowd Scoring
inferred← citeshttps://doi.org/10.1145/3308558.3313749openalex-snapshot
Improving Multiclass Classification in Crowdsourcing by Using Hierarchical Schemes
inferred← citeshttps://doi.org/10.1145/3397180openalex-snapshot
Quality-aware Online Task Assignment in Mobile Crowdsourcing
inferred← citeshttps://doi.org/10.1145/3534678.3539054openalex-snapshot
Affective Signals in a Social Media Recommender System
inferred← citeshttps://doi.org/10.1145/3696410.3714587openalex-snapshot
Query Design for Crowdsourced Clustering: Effect of Cognitive Overload and Contextual Bias
inferred← citeshttps://doi.org/10.1371/journal.pone.0156527openalex-snapshot
Crowdsourcing the Measurement of Interstate Conflict
inferred← citeshttps://doi.org/10.14778/2733085.2733101openalex-snapshot
Finish them!
inferred← citeshttps://doi.org/10.1561/9781680830910openalex-snapshot
Crowdsourced Data Management: Industry and Academic Perspectives
inferred← citeshttps://doi.org/10.1609/aaai.v29i1.9343openalex-snapshot
Acquiring Speech Transcriptions Using Mismatched Crowdsourcing
inferred← citeshttps://doi.org/10.1609/hcomp.v2i1.13170openalex-snapshot
Parallel Task Routing for Crowdsourcing
inferred← citeshttps://doi.org/10.18653/v1/k15-1020openalex-snapshot
Making the Most of Crowdsourced Document Annotations: Confused Supervised LDA
inferred← citeshttps://doi.org/10.24963/ijcai.2023/802openalex-snapshot
The Importance of Human-Labeled Data in the Era of LLMs
inferred← citeshttps://doi.org/10.3390/app11020630openalex-snapshot
Iterative Learning for K-Approval Votes in Crowdsourcing Systems
inferred← citeshttps://doi.org/10.48550/arxiv.1311.2972openalex-snapshot
Learning Mixtures of Discrete Product Distributions using Spectral Decompositions
inferred← citeshttps://doi.org/10.48550/arxiv.1406.3824openalex-snapshot
Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing
inferred← citeshttps://doi.org/10.48550/arxiv.1502.07710openalex-snapshot
Globally Optimal Crowdsourcing Quality Management
inferred← citeshttps://doi.org/10.48550/arxiv.1604.04928openalex-snapshot
Learning to Incentivize: Eliciting Effort via Output Agreement
inferred← citeshttps://doi.org/10.48550/arxiv.1612.07222openalex-snapshot
Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing
inferred← citeshttps://doi.org/10.48550/arxiv.1703.08085openalex-snapshot
Reducing Crowdsourcing to Graphon Estimation, Statistically
inferred← citeshttps://doi.org/10.48550/arxiv.1901.06237openalex-snapshot
BUOCA: Budget-Optimized Crowd Worker Allocation
inferred← citeshttps://doi.org/10.48550/arxiv.2012.15826openalex-snapshot
Educational Content Linking for Enhancing Learning Need Remediation in MOOCs
inferred← citeshttps://doi.org/10.48550/arxiv.2106.07193openalex-snapshot
Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization
inferred← citeshttps://doi.org/10.70675/a62b1982z8493z41d8za9d4zf9cd8881a7faopenalex-snapshot
Reliability in ensemble learning and learning from crowds
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