{"total":30,"rows":[{"id":"2601.09065","title":"Beyond Consensus: Perspectivist Modeling and Evaluation of Annotator Disagreement in NLP","created":"2026-01-14","cited_by_count":13,"area":null},{"id":"2203.04199","title":"Trustable Co-label Learning from Multiple Noisy Annotators","created":"2022-03-08","cited_by_count":12,"area":null},{"id":"2212.10405","title":"AnnoBERT: Effectively Representing Multiple Annotators' Label Choices to Improve Hate Speech Detection","created":"2022-12-20","cited_by_count":12,"area":null},{"id":"2306.03288","title":"Deep Learning From Crowdsourced Labels: Coupled Cross-entropy Minimization, Identifiability, and Regularization","created":"2023-06-05","cited_by_count":12,"area":null},{"id":"2306.03116","title":"Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds","created":"2023-06-05","cited_by_count":11,"area":null},{"id":"2312.07331","title":"Coupled Confusion Correction: Learning from Crowds with Sparse Annotations","created":"2023-12-12","cited_by_count":9,"area":null},{"id":"2304.02539","title":"Multi-annotator Deep Learning: A Probabilistic Framework for Classification","created":"2023-04-05","cited_by_count":8,"area":null},{"id":"2307.02191","title":"Evaluating AI systems under uncertain ground truth: a case study in dermatology","created":"2023-07-05","cited_by_count":7,"area":null},{"id":"2107.10449","title":"Improve Learning from Crowds via Generative Augmentation","created":"2021-07-22","cited_by_count":6,"area":null},{"id":"2205.11788","title":"Meta Policy Learning for Cold-Start Conversational Recommendation","created":"2022-05-24","cited_by_count":6,"area":null},{"id":"2411.17113","title":"Learning from Noisy Labels via Conditional Distributionally Robust Optimization","created":"2024-11-26","cited_by_count":6,"area":null},{"id":"2305.19391","title":"Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach","created":"2023-05-30","cited_by_count":5,"area":null},{"id":"2311.03153","title":"Architectural Sweet Spots for Modeling Human Label Variation by the Example of Argument Quality: It's Best to Relate Perspectives!","created":"2023-11-06","cited_by_count":5,"area":null},{"id":"2106.15146","title":"Learning from Multiple Annotators by Incorporating Instance Features","created":"2021-06-29","cited_by_count":4,"area":null},{"id":"2111.02326","title":"End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment Analysis","created":"2021-11-03","cited_by_count":4,"area":null},{"id":"2406.03356","title":"Cooperative learning of Pl@ntNet's Artificial Intelligence algorithm: how does it work and how can we improve it?","created":"2024-06-05","cited_by_count":4,"area":null},{"id":"2405.03386","title":"Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension","created":"2024-05-06","cited_by_count":3,"area":null},{"id":"2407.20950","title":"dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans","created":"2024-07-30","cited_by_count":3,"area":null},{"id":"2107.05039","title":"Learning from Crowds with Sparse and Imbalanced Annotations","created":"2021-07-11","cited_by_count":2,"area":null},{"id":"2302.03987","title":"Multiview Representation Learning from Crowdsourced Triplet Comparisons","created":"2023-02-08","cited_by_count":2,"area":null},{"id":"2604.07591","title":"From Ground Truth to Measurement: A Statistical Framework for Human Labeling","created":"2026-04-08","cited_by_count":2,"area":null},{"id":"2209.15380","title":"Identify ambiguous tasks combining crowdsourced labels by weighting Areas Under the Margin","created":"2022-09-30","cited_by_count":1,"area":null},{"id":"2103.02630","title":"Hypothesis Testing for Class-Conditional Label Noise","created":"2021-03-03","cited_by_count":0,"area":null},{"id":"2308.10396","title":"Label Selection Approach to Learning from Crowds","created":"2023-08-21","cited_by_count":0,"area":null},{"id":"2504.09085","title":"crowd-hpo: Realistic Hyperparameter Optimization and Benchmarking for Learning from Crowds with Noisy Labels","created":"2025-04-12","cited_by_count":0,"area":null},{"id":"2508.02123","title":"Understanding the Essence: Delving into Annotator Prototype Learning for Multi-Class Annotation Aggregation","created":"2025-08-04","cited_by_count":0,"area":null},{"id":"2601.20556","title":"Unsupervised Ensemble Learning Through Deep Energy-based Models","created":"2026-01-28","cited_by_count":0,"area":null},{"id":"2606.15554","title":"RaLMPH: Reliability-aware Learning for Multi-Pathologist Harmonization in Whole-Slide Image Classification","created":"2026-06-14","cited_by_count":0,"area":null},{"id":"2607.18465","title":"AHEAD: Advancing Multi-Class Label Aggregation with Interpretable Cross-Annotator Modeling","created":"2026-07-20","cited_by_count":0,"area":null},{"id":"2609.24028","title":"When Evidence Conflicts: Reliability-aware Meta-review Generation","created":"2026-09-21","cited_by_count":0,"area":null}],"next_cursor":null}