:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Li, Mengyang
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2502.07721
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Efficient Bilevel Optimization for Meta Label Correction in Noisy Label Learning
by: Nguyen, Ba Hoang Anh, et al.
Published: (2026)

Learning to Clean: Reinforcement Learning for Noisy Label Correction
by: Heidari, Marzi, et al.
Published: (2025)

Tackling Noisy Clients in Federated Learning with End-to-end Label Correction
by: Jiang, Xuefeng, et al.
Published: (2024)

Revisiting Meta-Learning with Noisy Labels: Reweighting Dynamics and Theoretical Guarantees
by: Zhang, Yiming, et al.
Published: (2025)

High-dimensional Learning with Noisy Labels
by: Firdoussi, Aymane El, et al.
Published: (2024)

Robust Self-Training with Closed-loop Label Correction for Learning from Noisy Labels
by: Lin, Zhanhui, et al.
Published: (2026)

Nested Graph Pseudo-Label Refinement for Noisy Label Domain Adaptation Learning
by: Wang, Yingxu, et al.
Published: (2025)

Online Multi-Label Classification under Noisy and Changing Label Distribution
by: Zou, Yizhang, et al.
Published: (2024)

FedEFC: Federated Learning Using Enhanced Forward Correction Against Noisy Labels
by: Yu, Seunghun, et al.
Published: (2025)

Addressing Long-Tail Noisy Label Learning Problems: a Two-Stage Solution with Label Refurbishment Considering Label Rarity
by: Wu, Ying-Hsuan, et al.
Published: (2024)

Learning Robust Reward Machines from Noisy Labels
by: Parac, Roko, et al.
Published: (2024)

Dirichlet-Based Prediction Calibration for Learning with Noisy Labels
by: Zong, Chen-Chen, et al.
Published: (2024)

FedNoisy: Federated Noisy Label Learning Benchmark
by: Liang, Siqi, et al.
Published: (2023)

Correcting Noisy Multilabel Predictions: Modeling Label Noise through Latent Space Shifts
by: Huang, Weipeng, et al.
Published: (2025)

CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels
by: Hu, Ruofan, et al.
Published: (2025)

Potential Energy based Mixture Model for Noisy Label Learning
by: Wang, Zijia, et al.
Published: (2024)

Learning with Noisy Labels through Learnable Weighting and Centroid Similarity
by: Wani, Farooq Ahmad, et al.
Published: (2023)

Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal
by: Sedova, Anastasiia, et al.
Published: (2023)

Learning from Noisy Labels for Long-tailed Data via Optimal Transport
by: Li, Mengting, et al.
Published: (2024)

Dynamical Label Augmentation and Calibration for Noisy Electronic Health Records
by: Li, Yuhao, et al.
Published: (2025)

When Noisy Labels Meet Class Imbalance on Graphs: A Graph Augmentation Method with LLM and Pseudo Label
by: Xia, Riting, et al.
Published: (2025)

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels
by: Penso, Coby, et al.
Published: (2025)

Balanced Edge Pruning for Graph Anomaly Detection with Noisy Labels
by: Wang, Zhu, et al.
Published: (2024)

Exploring the Robustness of In-Context Learning with Noisy Labels
by: Cheng, Chen, et al.
Published: (2024)

Invariant Correlation of Representation with Label: Enhancing Domain Generalization in Noisy Environments
by: Jin, Gaojie, et al.
Published: (2024)

Label Distribution Learning from Logical Label
by: Jia, Yuheng, et al.
Published: (2023)

Multi-Label Transfer Learning in Non-Stationary Data Streams
by: Du, Honghui, et al.
Published: (2025)

CleaR: Towards Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Label Learning
by: Kim, Yeachan, et al.
Published: (2024)

May the Forgetting Be with You: Alternate Replay for Learning with Noisy Labels
by: Millunzi, Monica, et al.
Published: (2024)

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition
by: Azad, Tarhib Al, et al.
Published: (2025)

CoLafier: Collaborative Noisy Label Purifier With Local Intrinsic Dimensionality Guidance
by: Zhang, Dongyu, et al.
Published: (2024)

Label Noise Robustness for Domain-Agnostic Fair Corrections via Nearest Neighbors Label Spreading
by: Stromberg, Nathan, et al.
Published: (2024)

NanoNet: Parameter-Efficient Learning with Label-Scarce Supervision for Lightweight Text Mining Model
by: Mao, Qianren, et al.
Published: (2026)

Federated Learning Client Pruning for Noisy Labels
by: Morafah, Mahdi, et al.
Published: (2024)

Fuzzy Label: From Concept to Its Application in Label Learning
by: Luoa, Chenxi, et al.
Published: (2025)

Ordinal Adaptive Correction: A Data-Centric Approach to Ordinal Image Classification with Noisy Labels
by: Moghaddam, Alireza Sedighi, et al.
Published: (2025)

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning
by: Xu, Jiayu, et al.
Published: (2026)

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

Class-based Subset Selection for Transfer Learning under Extreme Label Shift
by: Goyal, Akul, et al.
Published: (2024)

Learning From Crowdsourced Noisy Labels: A Signal Processing Perspective
by: Ibrahim, Shahana, et al.
Published: (2024)