label aggregation

Toward Annotator Group Bias in Crowdsourcing ACL 2022 Accounting for Confirmation Bias in Crowdsourced Label Aggregation IJCAI 2021 Learning from Crowds by Modeling Common Confusions AAAI 2021 On Releasing Annotator-Level Labels and Information in Datasets EMNLP 2021 Semi-Supervised Aggregation of Dependent Weak Supervision Sources With Performance Guarantees AISTATS 2021 skweak: Weak Supervision Made Easy for NLP ACL 2021 Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets CVPR 2021 Coupled-View Deep Classifier Learning from Multiple Noisy Annotators AAAI 2020 Efficient Pairwise Annotation of Argument Quality ACL 2020 Low Resource Sequence Tagging with Weak Labels AAAI 2020 Performance as a Constraint: An Improved Wisdom of Crowds Using Performance Regularization IJCAI 2020 Comparative judgments are more consistent than binary classification for labelling word complexity ACL 2019 Exploiting Worker Correlation for Label Aggregation in Crowdsourcing ICML 2019 Machine Learning with Crowdsourcing: A Brief Summary of the Past Research and Future Directions AAAI 2019 MiSC: Mixed Strategies Crowdsourcing IJCAI 2019 spark-crowd: A Spark Package for Learning from Crowdsourced Big Data JMLR 2019 Crowdsourcing with Arbitrary Adversaries ICML 2018 Aggregating Crowd Wisdoms with Label-aware Autoencoders IJCAI 2017 A Minimax Optimal Algorithm for Crowdsourcing NeurIPS 2017 A Stepwise Analysis of Aggregated Crowdsourced Labels Describing Multimodal Emotional Behaviors Interspeech 2017 Identifying Unreliable and Adversarial Workers in Crowdsourced Labeling Tasks JMLR 2017 Learning to Aggregate Ordinal Labels by Maximizing Separating Width ICML 2017 A Deep Learning Approach to Unsupervised Ensemble Learning ICML 2016 Exact Exponent in Optimal Rates for Crowdsourcing ICML 2016 Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing NeurIPS 2016