conftrace_

label aggregation

61 papers

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Co-occurring keywords

probabilistic modeling (852) noisy label (321) label noise (498) text classification (6793) annotator disagreement (65) annotator modeling (22) weak supervision (290) probabilistic model (476) expectation maximization (255) weakly supervised learning (1328)

Papers

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