Positive-Unlabeled Learning

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Positive-unlabeled learning studies how to classify or rank examples when some positives are labeled but the remaining data have unknown labels, rather than known negatives. Methods include risk estimators and class-prior correction, propensity weighting for biased label selection, and pseudo-labeling or representation learning for shifts and contaminated data. The approach is useful in settings such as anomaly and fraud detection, scientific discovery, and medical data, where reliable negative labels are scarce. A central challenge is that performance depends on assumptions about how positives were selected and how many positives are hidden among unlabeled examples; violations of these assumptions remain an important source of error.

Latest papers

23 papers0 papers in the last four weeks, down 100% on the four weeks before. 0.0% of all new papers.

Towards Label-Efficient Geospatial Foundation Model Adaptation via Positive-Unlabeled Learning for Building Damage Mapping
Nov 5, 2026Hengwei Zhao, Qirui Wang, Zhuo Zheng +3Geospatial Foundation Models Building Damage Assessment
Department of Ecology and Conservation Biology, Texas A&M University, College Station, 77843, Texas, USA · Department of Computer Science, Stanford University, Stanford, 94305, CA, USA · Department of Mechanical Engineering, National University of Singapore, Singapore, 117575, Singapore
Positive-Unlabeled Learning for Agent Safety False Alarm Auditing
Oct 2, 2026Xichen Yan, Chongyang Gao, Kezhen Chen +3AI Agent Safety Positive-Unlabeled Learning
Jinan University · Northwestern University · Analogy AI, Inc. +3
GeoCFM: Positive-Only Conditional Flow Matching for Mineral Occurrence Sampling
Sep 28, 2026Moshe Eliasof, Eldad HaberConditional Flow Matching Geospatial Reasoning
Faculty of Computer and Information Science, Ben-Gurion University of the Negev, Israel · Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Canada
Partial AUC Maximization from Positive-unlabeled Data
Sep 25, 2026Atsutoshi Kumagai, Tomoharu Iwata, Taishi Nishiyama +3Binary Classification Positive-Unlabeled Learning
NTT, Inc.
PU classification under Non-SCAR: clustering-assisted logistic model with oversampling enhancement
Sep 13, 2026Konrad Furmańczyk, Kacper PaczutkowskiPositive-Unlabeled Learning Weakly Supervised Learning
Institute of Information Technology, Warsaw University of Life Sciences, Warsaw, Poland
AUC Maximization from Biased Positive-unlabeled Data with Confidence
Sep 10, 2026Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi +3Class-Imbalanced Learning Positive-Unlabeled Learning
NTT, Inc.
Positive-Unlabeled Preference Optimization For Chest X-ray Report Generation
Aug 5, 2026Yuta Kobayashi, Pradyun Ramesh, Muhammad Ahmed Chaudhry +5Radiology Report Generation Radiology VLMs
Columbia University · Stanford University · Emory University
Transfer Learning for Avian Bioacoustics under Sparse Positive Labels
Aug 4, 2026Dhyey Patel, Yunting YinMulti-Source Domain Adaptation Weakly Supervised Learning
Eastern Michigan University Computer Science Department Ypsilanti, MI, USA
PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference
Jul 15, 2026Xutao Wang, Hanting Chen, Tianyu Guo +1Inverse Probability Weighting Positive-Unlabeled Learning
Huawei Noah’s Ark Lab
Quantifying and Auditing LLM Evaluation via Positive--Unlabeled Learning
Jun 17, 2026Zilong Zhang, Yi-Ting Hung, Lei Ding +1Inverse Optimal Transport LLM-as-a-Judge
Department of Mathematics and Statistics Georgia State University · Department of Statistics University of Manitoba
From Local Geometry to Global Pseudo Labeling for Robust Positive Unlabeled Learning under Covariate Shift
May 29, 2026Firas Gabetni, Alexandre Rocchi Henry, Nacim Belkhir +2Distribution Shift Robustness Covariate Shift
U2IS, ENSTA · Institut Polytechnique de Paris · AMIAD, Pôle Recherche, Palaiseau
SAGE: Scalable Automatic Gating Ensemble for Confident Negative Harvesting in Fraud Detection
May 19, 2026Sudheer Tubati, Amit GoyalEnsemble Learning Hard Negative Mining
Amazon Music Seattle, USA · Amazon Music San Francisco, USA
Focused PU learning from imbalanced data
May 14, 2026Elias Zavitsanos, Georgios PaliourasClass-Imbalanced Learning Binary Classification
Institute of Informatics and Telecommunications, NCSR “Demokritos”, Patriarhou Gregoriou E and 27 Neapoleos St., Agia Paraskevi, 15310, Attica, Greece.
Personalizing LLMs with Binary Feedback: A Preference-Corrected Optimization Framework
May 11, 2026Xilai Ma, Liye Zhao, Weijun Yao +3LLM Personalization User Preference Modeling
Harbin Institute of Technology, Shenzhen, China · Huawei Technologies Co., Ltd. · Nanyang Technological University
Better Protein Function Prediction by Modeling Survivorship Bias
May 7, 2026Zhongmou Chao, Poompol Buathong, Ekaterina Selivanovitch +2Protein Function Prediction Weakly Supervised Learning
Smith School of Chemical and Biomolecular Engineering, Cornell University, USA · Center for Applied Mathematics, Cornell University, USA · School of Operations Research and Information Engineering, Cornell University, USA
In-Context Positive-Unlabeled Learning
May 7, 2026Siyan Liu, Yi Chang, Manli Cheng +2Binary Classification Weakly Supervised Learning
Department of Statistics and Actuarial Science, University of Waterloo
ReLIC-SGG: Relation Lattice Completion for Open-Vocabulary Scene Graph Generation
Apr 24, 2026Amir Hosseini, Sara Farahani, Xinyi Li +1Scene Graph Generation Positive-Unlabeled Learning
Amirkabir University of Technology
Predicting Scale-Up of Metal-Organic Framework Syntheses with Large Language Models
Apr 21, 2026Peter Walther, Hongrui Sheng, Xinxin Liu +7AI-Assisted Scientific Research Materials Science
Department of Chemistry, Washington University, St. Louis, MO 63130, USA. · Department of Chemistry, Fudan University, Shanghai 200438, China. · Department of Computer and Information Science, University of Pennsylvania, PA 19104, USA. +2
A proposal for PU classification under Non-SCAR using clustering and logistic model
Apr 18, 2026Konrad Furmanczyk, Kacper PaczutkowskiLogistic Regression Weakly Supervised Learning
Institute of Information Technology, Warsaw University of Life Sciences, Warsaw, Poland
Detecting Media Clones in Cultural Repositories Using a Positive Unlabeled Learning Approach
Apr 5, 2026V. Sevetlidis, V. Arampatzakis, M. Karta +3Positive-Unlabeled Learning
Archimedes, ATHENA RC, Greece
A Unified and Stable Risk Minimization Framework for Weakly Supervised Learning with Theoretical Guarantees
Nov 28, 2025Miao Zhang, Junpeng Li, Changchun Hua +1Weakly Supervised Learning Statistical Learning Theory
Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, China
Synthesizability Prediction of Crystalline Structures with Structure-Aware Feature Learning and Uncertainty Quantification
Oct 22, 2025Danial Ebrahimzadeh, Sarah Sharif, Yaser Mike BanadMaterials Property Prediction Materials Science
School of Electrical and Computer Engineering, University of Oklahoma, 110 W. Boyd St., Norman, 73019, Oklahoma, USA.
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
May 29, 2024Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai +1Weakly Supervised Learning Semi-Supervised Anomaly Detection
NTT

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Towards Label-Efficient Geospatial Foundation Model Adaptation via Positive-Unlabeled Learning for Building Damage Mapping

Hengwei Zhao, Qirui Wang, Zhuo Zheng, Xiaoyan Lu+2

Department of Ecology and Conservation Biology, Texas A&M University, College Station, 77843, Texas, USA · Department of Computer Science, Stanford University, Stanford, 94305, CA, USA · Department of Mechanical Engineering, National University of Singapore, Singapore, 117575, Singapore

Geospatial Foundation Models Building Damage Assessment Weakly Supervised Learning Positive-Unlabeled Learning

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Geospatial foundation models (GFMs) are reshaping remote sensing by shifting the field from task-specific modeling toward general-purpose geospatial intelligence. Mapping the damage to buildings stands to benefit substantially from this shift, as GFMs pretrained on large-scale Earth observation data provide generalizable geospatial representations that can facilitate effective responses to sudden-onset disasters. Although parameter-efficient fine-tuning (PEFT) facilitates GFM adaptation, parameter efficiency does not necessarily translate into label efficiency. Building damage mapping remains challenging due to scarce pre-existing labeled data or limited time for annotation. When weak supervision is introduced to alleviate the labeling bottleneck, we identify the difficulty of learning sufficiently discriminative representations as a key challenge for label-efficient GFM adaptation. To address this challenge, we propose a Positive-Unlabeled GFM Adaptation (PUMA) framework that requires annotations for only a small number of damaged buildings. More specifically, PUMA integrates non-contrastive weakly supervised representation refinement to mitigate the impact of noisy supervision on representation learning from a gradient perspective, and latent-state-based label disambiguation to enable reversible supervision refinement without requiring class prior estimation. Extensive experiments on disaster benchmark datasets from around the world demonstrated the effectiveness and robustness of PUMA. A real-world application to the 2025 Los Angeles wildfires further highlighted its potential to accurately map damage under highly limited supervision. More broadly, these findings suggest that weakly supervised PEFT offers a label-efficient pathway from GFMs to real-world applications.