mlsanityData to 7 Oct 2026

Making Risk Minimization Tolerant to Label Noise

arXiv 1403.3610 ↗First posted 14 Mar 2014cs.LGcited by 54

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Abstract (arXiv)

In many applications, the training data, from which one needs to learn a classifier, is corrupted with label noise. Many standard algorithms such as SVM perform poorly in presence of label noise. In this paper we investigate the robustness of risk minimization to label noise. We prove a sufficient condition on a loss function for the risk minimization under that loss to be tolerant to uniform label noise. We show that the 0-1 loss, sigmoid loss, ramp loss and probit loss satisfy this condition though none of the standard convex loss functions satisfy it. We also prove that, by choosing a sufficiently large value of a parameter in the loss function, the sigmoid loss, ramp loss and probit loss can be made tolerant to non-uniform label noise also if we can assume the classes to be separable under noise-free data distribution. Through extensive empirical studies, we show that risk minimization under the 0-1 loss, the sigmoid loss and the ramp loss has much better robustness to label noise when compared to the SVM algorithm.

Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Mar 2014.

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Citing papers per year: 2021: 20; 2022: 10; 2023: 8; 2024: 4; 2025: 8; 2026: 4.

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  1. 2021Learning with Feature-Dependent Label Noise: A Progressive Approachcited by 102
  2. 2023The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecastingcited by 100
  3. 2022PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functionscited by 62
  4. 2021Asymmetric Loss Functions for Learning with Noisy Labelscited by 46
  5. 2022Robust Contrastive Learning against Noisy Viewscited by 45
  6. 2021Contrastive Learning Improves Model Robustness Under Label Noisecited by 36
  7. 2021Joint Negative and Positive Learning for Noisy Labelscited by 34
  8. 2022Benchmarking and Analyzing 3D Human Pose and Shape Estimation Beyond Algorithmscited by 28
  9. 2021On the Robustness of Average Losses for Partial-Label Learningcited by 20
  10. 2025Debiasing Multimodal Large Language Models via Noise-Aware Preference Optimizationcited by 14
  11. 2021SSR: An Efficient and Robust Framework for Learning with Unknown Label Noisecited by 13
  12. 2022Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teachercited by 12
38 more
  1. 2026Stable-DiffCoder: Pushing the Frontier of Code Diffusion Large Language Modelcited by 12
  2. 2021Robustness and Reliability When Training With Noisy Labelscited by 9
  3. 2021Weak Adaptation Learning -- Addressing Cross-domain Data Insufficiency with Weak Annotatorcited by 8
  4. 2024Label Noise: Ignorance Is Blisscited by 8
  5. 2025LLM-Powered Preference Elicitation in Combinatorial Assignmentcited by 7
  6. 2021Self-paced Resistance Learning against Overfitting on Noisy Labelscited by 6
  7. 2025Your contrastive learning problem is secretly a distribution alignment problemcited by 6
  8. 2021Unbiased Loss Functions for Multilabel Classification with Missing Labelscited by 5
  9. 2025SelectMix: Enhancing Label Noise Robustness through Targeted Sample Mixingcited by 5
  10. 2021Learning to Combat Noisy Labels via Classification Marginscited by 4
  11. 2021Multiplicative Reweighting for Robust Neural Network Optimizationcited by 4
  12. 2021Binary classification with corrupted labelscited by 4
  13. 2023Learning with Noisy Labels through Learnable Weighting and Centroid Similaritycited by 4
  14. 2023Cross-Video Contextual Knowledge Exploration and Exploitation for Ambiguity Reduction in Weakly Supervised Temporal Action Localizationcited by 4
  15. 2021Do We Really Need Gold Samples for Sample Weighting Under Label Noise?cited by 3
  16. 2022Multi-class Label Noise Learning via Loss Decomposition and Centroid Estimationcited by 3
  17. 2023Improve Noise Tolerance of Robust Loss via Noise-Awarenesscited by 3
  18. 2024Rejection via Learning Density Ratioscited by 3
  19. 2021Federated Learning for Intrusion Detection in IoT Security: A Hybrid Ensemble Approachcited by 2
  20. 2021Asymmetric error control under imperfect supervision: a label-noise-adjusted Neyman-Pearson umbrella algorithmcited by 2
  21. 2021The perils of being unhinged: On the accuracy of classifiers minimizing a noise-robust convex losscited by 2
  22. 2022STEdge: Self-training Edge Detection with Multi-layer Teaching and Regularizationcited by 2
  23. 2022Do We Need to Penalize Variance of Losses for Learning with Label Noise?cited by 2
  24. 2023Binary Classification with Instance and Label Dependent Label Noisecited by 2
  25. 2022The Fisher-Rao Loss for Learning under Label Noisecited by 1
  26. 2023Robust Loss Functions for Training Decision Trees with Noisy Labelscited by 1
  27. 2024Noisy Early Stopping for Noisy Labelscited by 1
  28. 2025DeGLIF for Label Noise Robust Node Classification using GNNscited by 1
  29. 2025Consistent View Alignment Improves Foundation Models for 3D Medical Image Segmentationcited by 1
  30. 2026Deconstructing the Failure of Ideal Noise Correction: A Three-Pillar Diagnosiscited by 1
  31. 2026Symmetrization of Loss Functions for Robust Training of Neural Networks in the Presence of Noisy Labelscited by 1
  32. 2026Smoothness-Based Derandomization of PAC-Bayes Boundscited by 1
  33. 2021A Symmetric Loss Perspective of Reliable Machine Learningcited by 0
  34. 2021Hypothesis Testing for Class-Conditional Label Noisecited by 0
  35. 2021Noisy Labels for Weakly Supervised Gamma Hadron Classificationcited by 0
  36. 2022Deep Learning with Label Noise: A Hierarchical Approachcited by 0
  37. 2022Performances of Symmetric Loss for Private Data from Exponential Mechanismcited by 0
  38. 2023RoLNiP: Robust Learning Using Noisy Pairwise Comparisonscited by 0

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