Cover Image

Mitigating Label Noise on Graph via Topological Sample Selection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wu, Yuhao, Yao, Jiangchao, Xia, Xiaobo, Yu, Jun, Wang, Ruxin, Han, Bo, Liu, Tongliang
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

Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning
by: Wu, Yuhao, et al.
Published: (2024)

Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples
by: Yuan, Suqin, et al.
Published: (2025)

Tackling Noisy Labels with Network Parameter Additive Decomposition
by: Wang, Jingyi, et al.
Published: (2024)

ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance
by: Chen, Ling-Hao, et al.
Published: (2023)

DeepInception: Hypnotize Large Language Model to Be Jailbreaker
by: Li, Xuan, et al.
Published: (2023)

Understanding and Mitigating the Bias in Sample Selection for Learning with Noisy Labels
by: Wei, Qi, et al.
Published: (2024)

Early Stopping Against Label Noise Without Validation Data
by: Yuan, Suqin, et al.
Published: (2025)

Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints
by: Xia, Xiaobo, et al.
Published: (2023)

Decoupling the Class Label and the Target Concept in Machine Unlearning
by: Zhu, Jianing, et al.
Published: (2024)

Label Noise Cleaning for Supervised Classification via Bernoulli Random Sampling
by: Liu, Yuxin, et al.
Published: (2026)

Omnimodal Dataset Distillation via High-order Proxy Alignment
by: Gao, Yuxuan, et al.
Published: (2026)

Robust Training of Federated Models with Extremely Label Deficiency
by: Zhang, Yonggang, et al.
Published: (2024)

Few-Shot Adversarial Prompt Learning on Vision-Language Models
by: Zhou, Yiwei, et al.
Published: (2024)

BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning
by: Zhang, Jingfeng, et al.
Published: (2023)

Noise-Resistant Label Reconstruction Feature Selection for Partial Multi-Label Learning
by: Gao, Wanfu, et al.
Published: (2025)

Understanding Robust Overfitting from the Feature Generalization Perspective
by: Yu, Chaojian, et al.
Published: (2023)

Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs
by: Zhou, Zhanke, et al.
Published: (2024)

SelectMix: Enhancing Label Noise Robustness through Targeted Sample Mixing
by: Liu, Qiuhao, et al.
Published: (2025)

Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel
by: Li, Xuan, et al.
Published: (2023)

Graph Neural Networks with Coarse- and Fine-Grained Division for Mitigating Label Sparsity and Noise
by: Li, Shuangjie, et al.
Published: (2024)

Graph Random Walk with Feature-Label Space Alignment: A Multi-Label Feature Selection Method
by: Gao, Wanfu, et al.
Published: (2025)

Resurrecting Label Propagation for Graphs with Heterophily and Label Noise
by: Cheng, Yao, et al.
Published: (2023)

On the Over-Memorization During Natural, Robust and Catastrophic Overfitting
by: Lin, Runqi, et al.
Published: (2023)

RewardFlow: Topology-Aware Reward Propagation on State Graphs for Agentic RL with Large Language Models
by: Feng, Xiao, et al.
Published: (2026)

Self-Calibrated Tuning of Vision-Language Models for Out-of-Distribution Detection
by: Yu, Geng, et al.
Published: (2024)

Resultant: Incremental Effectiveness on Likelihood for Unsupervised Out-of-Distribution Detection
by: Li, Yewen, et al.
Published: (2024)

Mitigating Label Noise through Data Ambiguation
by: Lienen, Julian, et al.
Published: (2023)

Differential-informed Sample Selection Accelerates Multimodal Contrastive Learning
by: Zhao, Zihua, et al.
Published: (2025)

Reference-guided Policy Optimization for Molecular Optimization via LLM Reasoning
by: Li, Xuan, et al.
Published: (2026)

Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency
by: Lin, Runqi, et al.
Published: (2024)

Per-parameter Task Arithmetic for Unlearning in Large Language Models
by: Cai, Chengyi, et al.
Published: (2026)

FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning
by: Ji, Xinyuan, et al.
Published: (2024)

Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds
by: Li, Shikun, et al.
Published: (2023)

Negative Label Guided OOD Detection with Pretrained Vision-Language Models
by: Jiang, Xue, et al.
Published: (2024)

Diversified Batch Selection for Training Acceleration
by: Hong, Feng, et al.
Published: (2024)

Multi-Label Adaptive Batch Selection by Highlighting Hard and Imbalanced Samples
by: Zhou, Ao, et al.
Published: (2024)

$ε$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise
by: Wang, Jialiang, et al.
Published: (2025)

What If the Input is Expanded in OOD Detection?
by: Zhang, Boxuan, et al.
Published: (2024)

Rethinking Model Selection in VLM Through the Lens of Gromov-Wasserstein Distance
by: Li, Muyang, et al.
Published: (2026)

FedRGL: Robust Federated Graph Learning for Label Noise
by: Li, De, et al.
Published: (2024)