Learning with Noisy Labels

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6 papers in the last four weeks, down 45% on the four weeks before. 0.1% of all new papers.

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Using binary silver labels in electronic health records-based computable phenotyping algorithms
Jul 20, 2026Shuhe Wang, Matthew T. Slaughter, Jennifer C. Nelson +1Weakly Supervised Learning Learning with Noisy Labels
Department of Biostatistics, University of Washington · Kaiser Permanente Center for Health Research · Biostatistics Division, Kaiser Permanente Washington Health Research Institute +1
Leveraging Dissimilarity Invariance as a Robust Anchor for Learning with Noisy Labels
Jul 20, 2026Wenxiao Fan, Kan LiMetric Learning Learning with Noisy Labels
School of Computer Science, Beijing Institute of Technology
Noise-Robust Box-Supervised Infrared Small Target Detection via Physics-Inspired Soft Label Optimization
Jul 19, 2026Xizhe Zhang, Fan Shi, Mianzhao Wang +3Soft-Label Learning Infrared Small Target Detection
Engineering Research Center of Learning-Based Intelligent System, Ministry of Education, Tianjin University of Technology, Tianjin, 300384, China · Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin, 300384, China · School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China
UR-VC: Unsupervised Robotic Value Correction for Time-Derived Progress Proxies
Jul 14, 2026Lirui Zhao, Modi Shi, Li Chen +3Long-Horizon Robotic Manipulation Learning with Noisy Labels
The University of Hong Kong
Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification
Jul 6, 2026Raphaël Bonnet-Guerrini, Bruno Sanchez, Dominique Fouchez +5Weakly Supervised Learning Learning with Noisy Labels
Università degli Studi di Milano, Department of Computer Science, Milan 20133, Italy · Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France · Université Côte d’Azur, INRIA, CNRS, Laboratoire J.A.Dieudonné, Maasai team, Nice 06000, France +2
Unbiased Alignment for Large Language Models with Noisy Preferences
Jul 3, 2026Jialiang Wang, Xianming Liu, Xiong Zhou +2Pairwise Preference Learning LLM Alignment
1Harbin Institute of Technology · 2City University of Hong Kong.
An automated method of identifying incorrectly labelled images based on the sequences of loss functions of deep learning networks
Jul 1, 2026Zhipeng Zhang, Wenhui Shou, Wengting Ma +5Noisy-Label Learning Learning with Noisy Labels
China Mobile Research Institute, Beijing 100032, China · Shenyang He Eye Hospital, Shenyang 110034, China
K-Inverse-RFM: A Modified RFM that Bridges the Gap to Neural Networks for Data-Corrupted Mathematical Tasks
Jul 1, 2026Gil PasternakKernel Methods Learning with Noisy Labels
UNIVERSITY OF CALIFORNIA SAN DIEGO
Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets
Jun 29, 2026Kaifeng Chen, Lechao Cheng, Jiyang Li +6Dataset Distillation Learning with Noisy Labels
Hefei University of Technology · Jianghuai Advance Technology Center · Anhui Provincial Key Laboratory of Humanoid Robots +2
Counting Trees from Satellite Imagery with Noisy Supervision
Jun 23, 2026Dimitri Gominski, Maurice Mugabowindekwe, Qiue Xu +6Remote Sensing Learning with Noisy Labels
University of Copenhagen · University of Rwanda · University of Chinese Academy of Sciences +3
Neural Architecture Search of Sample Reweighting Networks for Complex Distribution Shift
Jun 22, 2026Keisuke Sugawara, Kento Uchida, Shinichi ShirakawaClass-Imbalanced Learning Distribution Shift
Yokohama National University, Kanagawa, Japan
MOLAR: Learning Multimodal Molecular Representations from Noisy Labels
Jun 16, 2026Yingxu Wang, Kunyu Zhang, Nan Yin +2Molecular Property Prediction Learning with Noisy Labels
Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, AI Diyafah St, 7909, Abu Dhabi, United Arab Emirates · Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China · International College, Zhengzhou University, Daxue North Road, 450000, Henan, China +2
Learning Earthquake Wave Arrival Time Picking from Labels with Inaccuracies
Jun 13, 2026Sen Li, Xu Yang, S. Mostafa Mousavi +5Contrastive Learning Seismology
Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA. · School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China · School of Mines, China University of Mining and Technology, Xuzhou, Jiangsu, China +1
Benchmarking Instance-Dependent Label Noise with Controlled Corruptions
Jun 12, 2026Shadman Islam, Agustinus Kristiadi, Mostafa MilaniSynthetic Benchmark Generation Noisy-Label Learning
Department of Computer Science, Western University, London, Ontario, Canada
Explainable and Trustworthy Speech Emotion Recognition Using Confidence Score and Reinforcement Learning Rectified Speech Emotion Descriptors
Jun 12, 2026Youjun Chen, Xurong Xie, Mengzhe Geng +9Explainable Artificial Intelligence Reinforcement Learning
The Chinese University of Hong Kong, Hong Kong SAR, China · Institute of Software, Chinese Academy of Sciences, China · National Research Council Canada, Canada +1
Tight Boundary Prediction in Speaker Diarization Using Causal-Anticausal Consistency
Jun 10, 2026Shota Horiguchi, Marc Delcroix, Naohiro Tawara +2Speech Processing Learning with Noisy Labels
NTT, Inc., Japan
Noise-Aware Framework for Correcting Corrupted Labels
Jun 10, 2026Ha-Linh Nguyen, Hong-Anh Nguyen, Minh-Duc La +4Training Data Curation Noisy-Label Learning
Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam
RankVR: Low-Rank Structure Perception and Value Recalibration for Robust Composed Image Retrieval
Jun 10, 2026Jiale Huang, Zixu Li, Zhiheng Fu +3Learning with Noisy Labels Composed Image Retrieval
Shandong University Jinan, Shandong, China
Efficiently Learning Drifting Halfspaces with Massart Noise
Jun 9, 2026Mingchen Ma, Guyang Cao, Jelena Diakonikolas +1Noisy-Label Learning Learning with Noisy Labels
University of Wisconsin-Madison
Deep Active Re-Labeling: Toward Noise-Resilient Annotation Efficiency
Jun 7, 2026Md Abdullah Al Forhad, Weishi ShiHuman-in-the-Loop Annotation Active Learning
Active Machine Learning Lab · Learning · Language · Vision (LLaVi) Lab · Department of Computer Science and Engineering +1
An Adaptive Data cleaning Framework for Noisy Label Detection
Jun 5, 2026Chen-Hsuan Fang, Wei-Hsinag Chen, Pin-Hsuan Yu +2Noisy-Label Learning Learning with Noisy Labels
Department of Electrical Eng. National Taiwan Ocean University, Keelung City 20224, Taiwan · AI Research Center, National Taiwan Ocean University, Keelung City 20224, Taiwan
RQUL-UIE: Revitalizing Quality-Unstable Labels for Underwater Image Enhancement via In-Dataset Self-Supervision
Jun 4, 2026Haochen Hu, Yanrui Bin, Chih-yung Wen +1Diffusion Models for Image Restoration Underwater Image Enhancement
1The Hong Kong Polytechnic University
Intra-Modal Neighbors Never Lie: Rectifying Inter-Modal Noisy Correspondence via Graph-Based Intra-Modal Reasoning
Jun 2, 2026Yang Liu, Wentao Feng, Shu-Dong Huang +2Cross-Modal Alignment Cross-Modal Retrieval
College of Computer Science, Sichuan University, China · School of Computer Sci
Machine Learning for Coding Retail Product Names to Consumer-Price Categories: A Rule-plus-Bag-of-Words Pipeline with Reliability-Weighted Human-in-the-Loop Labeling
Jun 1, 2026Vladimir BeskorovainyiHuman-in-the-Loop Annotation Text Classification
Besk Tech · Moscow Institute of Physics and Technology (MIPT)
Trust Functions: Near-Lossless Weak-to-Strong Generalization by Learning When to Trust the Weak Teacher
May 31, 2026Arda Uzunoglu, Alvin Zhang, Daniel KhashabiData Selection Training Data Selection
Department of Computer Science, Johns Hopkins University, Maryland, United States.
Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance
May 30, 2026Jiancheng Zhang, Meiqing Li, Qi Zhang +1Class-Imbalanced Learning Active Learning
University of California, Riverside · 2Carnegie Mellon University · 3Worcester Polytechnic Institute
Fraud Type Decomposition and the Observation-Mechanism Taxonomy:Class-Specific Detection Limits in Payment Networks
May 29, 2026Gaurav DhamaFinancial Fraud Detection Learning with Noisy Labels
Causal Label Recovery in Payment Networks
May 28, 2026Gaurav DhamaFinancial Fraud Detection Learning with Noisy Labels
Mastercard
Unification and Optimization of Robust Supervised Learning
May 27, 2026Jonas Hanselle, Valentin Margraf, Clemens Damke +1Distribution Shift Robustness Robust Optimization
LMU Munich, MCML · LMU Munich, MCML, DFKI
Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMs
May 27, 2026Safal Thapaliya, Jiatan Huang, Chuxu ZhangLLM-Assisted Annotation Text-Attributed Graph Learning
University of Connecticut, USA

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Using binary silver labels in electronic health records-based computable phenotyping algorithms

Shuhe Wang, Matthew T. Slaughter, Jennifer C. Nelson, Brian D. Williamson

Department of Biostatistics, University of Washington · Kaiser Permanente Center for Health Research · Biostatistics Division, Kaiser Permanente Washington Health Research Institute+1

Weakly Supervised Learning Learning with Noisy Labels Electronic Health Records

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Gold-standard phenotype labels are often unavailable at scale in electronic health record (EHR) studies because they require manual chart review. Weakly supervised phenotyping methods instead use silver-standard labels, such as diagnosis-code counts, natural language processing (NLP) mentions, medication indicators, or laboratory thresholds. PheNorm is widely used for this purpose, but its original formulation was designed for count-valued silver labels and relies on log transformation, utilization normalization, and Gaussian mixture modeling. These steps are not directly suited to binary silver labels, which are common and may be highly informative. We propose Binary PheNorm, an extension that uses binary silver labels directly in the corruption-and-regression denoising step and produces a continuous phenotype score without EM calibration. We also consider a lasso-regularized version for high-dimensional EHR settings and combined models using both binary and count labels. In simulations, Binary PheNorm achieved strong discrimination using binary labels alone and often improved performance when combined with count labels. In anaphylaxis, AUC increased from 0.793 for an epinephrine-mention indicator to 0.891-0.892 after Binary PheNorm. In acute pancreatitis, AUC increased from 0.736 for a lipase-threshold indicator to 0.805-0.819. These results support Binary PheNorm as a practical weakly supervised approach when informative binary silver labels are available.

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