{"total":21,"rows":[{"id":"2103.14749","title":"Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks","created":"2021-03-26","cited_by_count":395,"area":null},{"id":"2206.02791","title":"Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation","created":"2022-06-06","cited_by_count":49,"area":null},{"id":"2111.14932","title":"Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection","created":"2021-11-29","cited_by_count":20,"area":null},{"id":"2407.13722","title":"Enhanced $H$-Consistency Bounds","created":"2024-07-18","cited_by_count":18,"area":null},{"id":"2309.10979","title":"Towards Data-centric Graph Machine Learning: Review and Outlook","created":"2023-09-20","cited_by_count":15,"area":null},{"id":"2101.09763","title":"Analysing the Noise Model Error for Realistic Noisy Label Data","created":"2021-01-24","cited_by_count":14,"area":null},{"id":"2211.05764","title":"DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems","created":"2022-11-09","cited_by_count":14,"area":null},{"id":"2305.16771","title":"Robust Nonparametric Regression under Poisoning Attack","created":"2023-05-26","cited_by_count":11,"area":null},{"id":"2106.09136","title":"Binary classification with corrupted labels","created":"2021-06-16","cited_by_count":4,"area":null},{"id":"2104.09045","title":"Do We Really Need Gold Samples for Sample Weighting Under Label Noise?","created":"2021-04-19","cited_by_count":3,"area":null},{"id":"2203.10858","title":"Multi-class Label Noise Learning via Loss Decomposition and Centroid Estimation","created":"2022-03-21","cited_by_count":3,"area":null},{"id":"2210.11327","title":"Improving Data Quality with Training Dynamics of Gradient Boosting Decision Trees","created":"2022-10-20","cited_by_count":3,"area":null},{"id":"2402.01055","title":"Multiclass Learning from Noisy Labels for Non-decomposable Performance Measures","created":"2024-02-01","cited_by_count":3,"area":null},{"id":"2204.12155","title":"Bias-Variance Decompositions for Margin Losses","created":"2022-04-26","cited_by_count":2,"area":null},{"id":"2405.18929","title":"Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data","created":"2024-05-29","cited_by_count":2,"area":null},{"id":"2103.13607","title":"Exploiting Class Similarity for Machine Learning with Confidence Labels and Projective Loss Functions","created":"2021-03-25","cited_by_count":1,"area":null},{"id":"2311.11473","title":"CSGNN: Conquering Noisy Node labels via Dynamic Class-wise Selection","created":"2023-11-20","cited_by_count":1,"area":null},{"id":"2407.04029","title":"Robust Learning under Hybrid Noise","created":"2024-07-04","cited_by_count":1,"area":null},{"id":"2605.20347","title":"Symmetrization of Loss Functions for Robust Training of Neural Networks in the Presence of Noisy Labels","created":"2026-05-19","cited_by_count":1,"area":null},{"id":"2206.13025","title":"Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels","created":"2022-06-27","cited_by_count":0,"area":null},{"id":"2601.19947","title":"NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning","created":"2026-01-24","cited_by_count":0,"area":null}],"next_cursor":null}