{"total":53,"rows":[{"id":"2103.07756","title":"Learning with Feature-Dependent Label Noise: A Progressive Approach","created":"2021-03-13","cited_by_count":102,"area":null},{"id":"2304.05206","title":"The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting","created":"2023-04-11","cited_by_count":101,"area":null},{"id":"2204.12511","title":"PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions","created":"2022-04-26","cited_by_count":63,"area":null},{"id":"2106.03110","title":"Asymmetric Loss Functions for Learning with Noisy Labels","created":"2021-06-06","cited_by_count":46,"area":null},{"id":"2201.04309","title":"Robust Contrastive Learning against Noisy Views","created":"2022-01-12","cited_by_count":45,"area":null},{"id":"2104.08984","title":"Contrastive Learning Improves Model Robustness Under Label Noise","created":"2021-04-19","cited_by_count":36,"area":null},{"id":"2104.06574","title":"Joint Negative and Positive Learning for Noisy Labels","created":"2021-04-14","cited_by_count":34,"area":null},{"id":"2209.10529","title":"Benchmarking and Analyzing 3D Human Pose and Shape Estimation Beyond Algorithms","created":"2022-09-21","cited_by_count":28,"area":null},{"id":"2106.06152","title":"On the Robustness of Average Losses for Partial-Label Learning","created":"2021-06-11","cited_by_count":20,"area":null},{"id":"2111.11288","title":"SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise","created":"2021-11-22","cited_by_count":13,"area":null},{"id":"2601.15892","title":"Stable-DiffCoder: Pushing the Frontier of Code Diffusion Large Language Model","created":"2026-01-22","cited_by_count":13,"area":null},{"id":"2205.14361","title":"Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher","created":"2022-05-28","cited_by_count":12,"area":null},{"id":"2110.03321","title":"Robustness and Reliability When Training With Noisy Labels","created":"2021-10-07","cited_by_count":10,"area":null},{"id":"2411.00079","title":"Label Noise: Ignorance Is Bliss","created":"2024-10-31","cited_by_count":9,"area":null},{"id":"2102.07358","title":"Weak Adaptation Learning -- Addressing Cross-domain Data Insufficiency with Weak Annotator","created":"2021-02-15","cited_by_count":8,"area":null},{"id":"2502.10308","title":"LLM-Powered Preference Elicitation in Combinatorial Assignment","created":"2025-02-14","cited_by_count":8,"area":null},{"id":"2105.03059","title":"Self-paced Resistance Learning against Overfitting on Noisy Labels","created":"2021-05-07","cited_by_count":6,"area":null},{"id":"2502.20141","title":"Your contrastive learning problem is secretly a distribution alignment problem","created":"2025-02-27","cited_by_count":6,"area":null},{"id":"2109.11282","title":"Unbiased Loss Functions for Multilabel Classification with Missing Labels","created":"2021-09-23","cited_by_count":5,"area":null},{"id":"2509.11265","title":"SelectMix: Enhancing Label Noise Robustness through Targeted Sample Mixing","created":"2025-09-14","cited_by_count":5,"area":null},{"id":"2102.00751","title":"Learning to Combat Noisy Labels via Classification Margins","created":"2021-02-01","cited_by_count":4,"area":null},{"id":"2102.12192","title":"Multiplicative Reweighting for Robust Neural Network Optimization","created":"2021-02-24","cited_by_count":4,"area":null},{"id":"2106.09136","title":"Binary classification with corrupted labels","created":"2021-06-16","cited_by_count":4,"area":null},{"id":"2303.09470","title":"Learning with Noisy Labels through Learnable Weighting and Centroid Similarity","created":"2023-03-16","cited_by_count":4,"area":null},{"id":"2308.12609","title":"Cross-Video Contextual Knowledge Exploration and Exploitation for Ambiguity Reduction in Weakly Supervised Temporal Action Localization","created":"2023-08-24","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":"2112.00314","title":"Asymmetric error control under imperfect supervision: a label-noise-adjusted Neyman-Pearson umbrella algorithm","created":"2021-12-01","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":"2301.07306","title":"Improve Noise Tolerance of Robust Loss via Noise-Awareness","created":"2023-01-18","cited_by_count":3,"area":null},{"id":"2405.18686","title":"Rejection via Learning Density Ratios","created":"2024-05-29","cited_by_count":3,"area":null},{"id":"2106.15349","title":"Federated Learning for Intrusion Detection in IoT Security: A Hybrid Ensemble Approach","created":"2021-06-25","cited_by_count":2,"area":null},{"id":"2112.04590","title":"The perils of being unhinged: On the accuracy of classifiers minimizing a noise-robust convex loss","created":"2021-12-08","cited_by_count":2,"area":null},{"id":"2201.05121","title":"STEdge: Self-training Edge Detection with Multi-layer Teaching and Regularization","created":"2022-01-13","cited_by_count":2,"area":null},{"id":"2201.12739","title":"Do We Need to Penalize Variance of Losses for Learning with Label Noise?","created":"2022-01-30","cited_by_count":2,"area":null},{"id":"2306.03402","title":"Binary Classification with Instance and Label Dependent Label Noise","created":"2023-06-06","cited_by_count":2,"area":null},{"id":"2210.16401","title":"The Fisher-Rao Loss for Learning under Label Noise","created":"2022-10-28","cited_by_count":1,"area":null},{"id":"2312.12937","title":"Robust Loss Functions for Training Decision Trees with Noisy Labels","created":"2023-12-20","cited_by_count":1,"area":null},{"id":"2409.06830","title":"Noisy Early Stopping for Noisy Labels","created":"2024-09-10","cited_by_count":1,"area":null},{"id":"2506.00244","title":"DeGLIF for Label Noise Robust Node Classification using GNNs","created":"2025-05-30","cited_by_count":1,"area":null},{"id":"2509.13846","title":"Consistent View Alignment Improves Foundation Models for 3D Medical Image Segmentation","created":"2025-09-17","cited_by_count":1,"area":null},{"id":"2603.12997","title":"Deconstructing the Failure of Ideal Noise Correction: A Three-Pillar Diagnosis","created":"2026-03-13","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":"2606.19105","title":"Smoothness-Based Derandomization of PAC-Bayes Bounds","created":"2026-06-17","cited_by_count":1,"area":null},{"id":"2101.01366","title":"A Symmetric Loss Perspective of Reliable Machine Learning","created":"2021-01-05","cited_by_count":0,"area":null},{"id":"2103.02630","title":"Hypothesis Testing for Class-Conditional Label Noise","created":"2021-03-03","cited_by_count":0,"area":null},{"id":"2108.13396","title":"Noisy Labels for Weakly Supervised Gamma Hadron Classification","created":"2021-08-30","cited_by_count":0,"area":null},{"id":"2205.14299","title":"Deep Learning with Label Noise: A Hierarchical Approach","created":"2022-05-28","cited_by_count":0,"area":null},{"id":"2210.04132","title":"Performances of Symmetric Loss for Private Data from Exponential Mechanism","created":"2022-10-09","cited_by_count":0,"area":null},{"id":"2303.02341","title":"RoLNiP: Robust Learning Using Noisy Pairwise Comparisons","created":"2023-03-04","cited_by_count":0,"area":null},{"id":"2310.15999","title":"Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships","created":"2023-10-24","cited_by_count":0,"area":null},{"id":"2402.01797","title":"Robust support vector machines via conic optimization","created":"2024-02-02","cited_by_count":0,"area":null},{"id":"2511.16512","title":"Hard Samples, Bad Labels: Robust Loss Functions That Know When to Back Off","created":"2025-11-20","cited_by_count":0,"area":null},{"id":"2511.20826","title":"Effects of Initialization Biases on Deep Neural Network Training Dynamics","created":"2025-11-25","cited_by_count":0,"area":null}],"next_cursor":null}