Soft Label PU Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Zhao, Puning, Deng, Jintao, Cheng, Xu |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Minimax Optimal Q Learning with Nearest Neighbors
by: Zhao, Puning, et al.
Published: (2023)
by: Zhao, Puning, et al.
Published: (2023)
Dens-PU: PU Learning with Density-Based Positive Labeled Augmentation
by: Sevetlidis, Vasileios, et al.
Published: (2023)
by: Sevetlidis, Vasileios, et al.
Published: (2023)
On Theoretical Limits of Learning with Label Differential Privacy
by: Zhao, Puning, et al.
Published: (2025)
by: Zhao, Puning, et al.
Published: (2025)
Enhancing Learning with Label Differential Privacy by Vector Approximation
by: Zhao, Puning, et al.
Published: (2024)
by: Zhao, Puning, et al.
Published: (2024)
H+: An Efficient Similarity-Aware Aggregation for Byzantine Resilient Federated Learning
by: Zuo, Shiyuan, et al.
Published: (2025)
by: Zuo, Shiyuan, et al.
Published: (2025)
A Huber Loss Minimization Approach to Byzantine Robust Federated Learning
by: Zhao, Puning, et al.
Published: (2023)
by: Zhao, Puning, et al.
Published: (2023)
Exploiting the Potential Supervision Information of Clean Samples in Partial Label Learning
by: Wang, Guangtai, et al.
Published: (2025)
by: Wang, Guangtai, et al.
Published: (2025)
Focused PU learning from imbalanced data
by: Zavitsanos, Elias, et al.
Published: (2026)
by: Zavitsanos, Elias, et al.
Published: (2026)
Label Learning Method Based on Tensor Projection
by: Li, Jing, et al.
Published: (2024)
by: Li, Jing, et al.
Published: (2024)
Learning with User-Level Local Differential Privacy
by: Zhao, Puning, et al.
Published: (2024)
by: Zhao, Puning, et al.
Published: (2024)
Learning with Confidence: Training Better Classifiers from Soft Labels
by: de Vries, Sjoerd, et al.
Published: (2024)
by: de Vries, Sjoerd, et al.
Published: (2024)
Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU) Learning in Cybersecurity
by: Dilworth, Robert, et al.
Published: (2024)
by: Dilworth, Robert, et al.
Published: (2024)
Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasets
by: Yan, Xingrun, et al.
Published: (2024)
by: Yan, Xingrun, et al.
Published: (2024)
Rethinking Self-Distillation: Label Averaging and Enhanced Soft Label Refinement with Partial Labels
by: Jeong, Hyeonsu, et al.
Published: (2024)
by: Jeong, Hyeonsu, et al.
Published: (2024)
FedNoisy: Federated Noisy Label Learning Benchmark
by: Liang, Siqi, et al.
Published: (2023)
by: Liang, Siqi, et al.
Published: (2023)
Consistently Informative Soft-Label Temperature for Knowledge Distillation
by: Luong, Hoang-Chau, et al.
Published: (2026)
by: Luong, Hoang-Chau, et al.
Published: (2026)
Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression
by: Pu, Ruizhi, et al.
Published: (2024)
by: Pu, Ruizhi, et al.
Published: (2024)
Generating the Ground Truth: Synthetic Data for Soft Label and Label Noise Research
by: de Vries, Sjoerd, et al.
Published: (2023)
by: de Vries, Sjoerd, et al.
Published: (2023)
Harnessing PU Learning for Enhanced Cloud-based DDoS Detection: A Comparative Analysis
by: Dilworth, Robert, et al.
Published: (2024)
by: Dilworth, Robert, et al.
Published: (2024)
Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
by: Zuo, Shiyuan, et al.
Published: (2024)
by: Zuo, Shiyuan, et al.
Published: (2024)
Soft-Label Integration for Robust Toxicity Classification
by: Cheng, Zelei, et al.
Published: (2024)
by: Cheng, Zelei, et al.
Published: (2024)
Probabilistic Label Spreading: Efficient and Consistent Estimation of Soft Labels with Epistemic Uncertainty on Graphs
by: Klees, Jonathan, et al.
Published: (2026)
by: Klees, Jonathan, et al.
Published: (2026)
GBRIP: Granular Ball Representation for Imbalanced Partial Label Learning
by: Huang, Jintao, et al.
Published: (2024)
by: Huang, Jintao, et al.
Published: (2024)
Feature selection based on cluster assumption in PU learning
by: Uchikoshi, Motonobu, et al.
Published: (2025)
by: Uchikoshi, Motonobu, et al.
Published: (2025)
Accurate Link Prediction for Edge-Incomplete Graphs via PU Learning
by: Kim, Junghun, et al.
Published: (2024)
by: Kim, Junghun, et al.
Published: (2024)
Scalable Label Distribution Learning for Multi-Label Classification
by: Zhao, Xingyu, et al.
Published: (2023)
by: Zhao, Xingyu, et al.
Published: (2023)
Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach
by: Hassouna, Mohamed, et al.
Published: (2025)
by: Hassouna, Mohamed, et al.
Published: (2025)
Understanding Contrastive Representation Learning from Positive Unlabeled (PU) Data
by: Acharya, Anish, et al.
Published: (2024)
by: Acharya, Anish, et al.
Published: (2024)
Soft-Label Caching and Sharpening for Communication-Efficient Federated Distillation
by: Azuma, Kitsuya, et al.
Published: (2025)
by: Azuma, Kitsuya, et al.
Published: (2025)
The Bridge-Garden Dilemma in LLM Distillation: Why Mixing Hard and Soft Labels Works
by: Wang, Guanghui, et al.
Published: (2026)
by: Wang, Guanghui, et al.
Published: (2026)
Learning New Tasks from a Few Examples with Soft-Label Prototypes
by: Singh, Avyav Kumar, et al.
Published: (2022)
by: Singh, Avyav Kumar, et al.
Published: (2022)
Contextual Bandits for Unbounded Context Distributions
by: Zhao, Puning, et al.
Published: (2024)
by: Zhao, Puning, et al.
Published: (2024)
Soft-Label Training Preserves Epistemic Uncertainty
by: Singh, Agamdeep, et al.
Published: (2025)
by: Singh, Agamdeep, et al.
Published: (2025)
Consistent Estimation of Numerical Distributions under Local Differential Privacy by Wavelet Expansion
by: Zhao, Puning, et al.
Published: (2025)
by: Zhao, Puning, et al.
Published: (2025)
Collaborative Learning with Different Labeling Functions
by: Deng, Yuyang, et al.
Published: (2024)
by: Deng, Yuyang, et al.
Published: (2024)
A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy
by: Zhao, Puning, et al.
Published: (2024)
by: Zhao, Puning, et al.
Published: (2024)
A proposal for PU classification under Non-SCAR using clustering and logistic model
by: Furmanczyk, Konrad, et al.
Published: (2026)
by: Furmanczyk, Konrad, et al.
Published: (2026)
Federated Learning Resilient to Byzantine Attacks and Data Heterogeneity
by: Zuo, Shiyuan, et al.
Published: (2024)
by: Zuo, Shiyuan, et al.
Published: (2024)
An Assessment of Human vs. Model Uncertainty in Soft-Label Learning and Calibration
by: Pavlovic, Maja, et al.
Published: (2026)
by: Pavlovic, Maja, et al.
Published: (2026)
Learning from Concealed Labels
by: Li, Zhongnian, et al.
Published: (2024)
by: Li, Zhongnian, et al.
Published: (2024)
Similar Items
-
Minimax Optimal Q Learning with Nearest Neighbors
by: Zhao, Puning, et al.
Published: (2023) -
Dens-PU: PU Learning with Density-Based Positive Labeled Augmentation
by: Sevetlidis, Vasileios, et al.
Published: (2023) -
On Theoretical Limits of Learning with Label Differential Privacy
by: Zhao, Puning, et al.
Published: (2025) -
Enhancing Learning with Label Differential Privacy by Vector Approximation
by: Zhao, Puning, et al.
Published: (2024) -
H+: An Efficient Similarity-Aware Aggregation for Byzantine Resilient Federated Learning
by: Zuo, Shiyuan, et al.
Published: (2025)