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2502.07551
Cited By
Early Stopping Against Label Noise Without Validation Data
International Conference on Learning Representations (ICLR), 2025
11 February 2025
Suqin Yuan
Lei Feng
Tongliang Liu
NoLa
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Papers citing
"Early Stopping Against Label Noise Without Validation Data"
50 / 74 papers shown
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Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization
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Denis Huseljic
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346
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Mitigating Label Noise on Graph via Topological Sample Selection
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Regularly Truncated M-estimators for Learning with Noisy Labels
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197
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Late Stopping: Avoiding Confidently Learning from Mislabeled Examples
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Lei Feng
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268
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Jun Shu
Deyu Meng
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264
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Progress measures for grokking via mechanistic interpretability
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Neel Nanda
Lawrence Chan
Tom Lieberum
Jess Smith
Jacob Steinhardt
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Leveraging Unlabeled Data to Track Memorization
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Mahsa Forouzesh
Hanie Sedghi
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207
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Characterizing Datapoints via Second-Split Forgetting
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Saurabh Garg
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Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization
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185
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Towards Understanding Grokking: An Effective Theory of Representation Learning
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O. Kitouni
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SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labels
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Yangdi Lu
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Robust Training under Label Noise by Over-parameterization
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Zhihui Zhu
Qing Qu
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Gang Niu
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Understanding and Improving Early Stopping for Learning with Noisy Labels
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Jiatong Li
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214
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To Smooth or Not? When Label Smoothing Meets Noisy Labels
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Hangyu Liu
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Gang Niu
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626
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RATT: Leveraging Unlabeled Data to Guarantee Generalization
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237
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