Cover Image

PUAL: A Classifier on Trifurcate Positive-Unlabeled Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Xiaoke, Yang, Xiaochen, Zhu, Rui, Xue, Jing-Hao
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On Leveraging Unlabeled Data for Concurrent Positive-Unlabeled Classification and Robust Generation
by: Yu, Bing, et al.
Published: (2020)

Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
by: Takahashi, Hiroshi, et al.
Published: (2024)

Heterogeneous Domain Adaptation with Positive and Unlabeled Data
by: Mori, Junki, et al.
Published: (2023)

Positive Unlabeled Contrastive Learning
by: Acharya, Anish, et al.
Published: (2022)

Unlabeled Data Can Provably Enhance In-Context Learning of Transformers
by: Liu, Renpu, et al.
Published: (2026)

Learning from M-Tuple Dominant Positive and Unlabeled Data
by: Qin, Jiahe, et al.
Published: (2025)

Positive and Unlabeled Data: Model, Estimation, Inference, and Classification
by: Liu, Siyan, et al.
Published: (2024)

In-Context Positive-Unlabeled Learning
by: Liu, Siyan, et al.
Published: (2026)

Positive-Unlabeled Diffusion Models for Preventing Sensitive Data Generation
by: Takahashi, Hiroshi, et al.
Published: (2025)

Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU) Learning in Cybersecurity
by: Dilworth, Robert, et al.
Published: (2024)

Heterogeneous Multisource Transfer Learning via Model Averaging for Positive-Unlabeled Data
by: Liu, Jialei, et al.
Published: (2025)

Automated Machine Learning for Positive-Unlabelled Learning
by: Saunders, Jack D., et al.
Published: (2024)

Understanding Contrastive Representation Learning from Positive Unlabeled (PU) Data
by: Acharya, Anish, et al.
Published: (2024)

Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithms
by: Wang, Wei, et al.
Published: (2025)

Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends
by: Wang, Xinrui, et al.
Published: (2023)

PSPU: Enhanced Positive and Unlabeled Learning by Leveraging Pseudo Supervision
by: Wang, Chengjie, et al.
Published: (2024)

Unlabeled Data vs. Pre-trained Knowledge: Rethinking SSL in the Era of Large Models
by: Lv, Song-Lin, et al.
Published: (2025)

Angular Regularization for Positive-Unlabeled Learning on the Hypersphere
by: Sevetlidis, Vasileios, et al.
Published: (2025)

Noisy-Pair Robust Representation Alignment for Positive-Unlabeled Learning
by: Zhao, Hengwei, et al.
Published: (2025)

Semi-Supervised Conformal Prediction With Unlabeled Nonconformity Score
by: Zhou, Xuanning, et al.
Published: (2025)

ESA: Example Sieve Approach for Multi-Positive and Unlabeled Learning
by: Li, Zhongnian, et al.
Published: (2024)

Verifying the Selected Completely at Random Assumption in Positive-Unlabeled Learning
by: Teisseyre, Paweł, et al.
Published: (2024)

Adapting to Shifting Correlations with Unlabeled Data Calibration
by: Nguyen, Minh, et al.
Published: (2024)

Uncertainty-aware Pseudo-label Selection for Positive-Unlabeled Learning
by: Dorigatti, Emilio, et al.
Published: (2022)

Positive-Unlabeled Reinforcement Learning Distillation for On-Premise Small Models
by: Kou, Zhiqiang, et al.
Published: (2026)

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification
by: Kato, Masahiro, et al.
Published: (2025)

Semi-Supervised Crowd Counting from Unlabeled Data
by: Duan, Haoran, et al.
Published: (2021)

Proper Learnability and the Role of Unlabeled Data
by: Asilis, Julian, et al.
Published: (2025)

Augmenting Offline RL with Unlabeled Data
by: Wang, Zhao, et al.
Published: (2024)

M-Loss: Quantifying Model Merging Compatibility with Limited Unlabeled Data
by: Wang, Tiantong, et al.
Published: (2026)

An Effective Flow-based Method for Positive-Unlabeled Learning: 2-HNC
by: Hochbaum, Dorit, et al.
Published: (2025)

Positive-Unlabeled Learning for Control Group Construction in Observational Causal Inference
by: Tsoumas, Ilias, et al.
Published: (2025)

Cost-Sensitive Unbiased Risk Estimation for Multi-Class Positive-Unlabeled Learning
by: Zhang, Miao, et al.
Published: (2025)

Collaborative Unlabeled Data Optimization
by: Shang, Xinyi, et al.
Published: (2025)

Offline Reinforcement Learning with Domain-Unlabeled Data
by: Nishimori, Soichiro, et al.
Published: (2024)

Out-Of-Domain Unlabeled Data Improves Generalization
by: Saberi, Amir Hossein, et al.
Published: (2023)

Sample-Optimal Agnostic Boosting with Unlabeled Data
by: Ghai, Udaya, et al.
Published: (2025)

Learning from Uncertain Similarity and Unlabeled Data
by: Wei, Meng, et al.
Published: (2025)

Coupled Training with Privileged Information and Unlabeled Data
by: Shi, Jiahao, et al.
Published: (2026)

Document Set Expansion with Positive-Unlabeled Learning: A Density Estimation-based Approach
by: Zhang, Haiyang, et al.
Published: (2024)