Cover Image

Learning from Noisy Labels for Long-tailed Data via Optimal Transport

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Mengting, Zhu, Chuang
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Noisy Label Processing for Classification: A Survey
by: Li, Mengting, et al.
Published: (2024)

Optimal Transport for LLM Reward Modeling from Noisy Preference
by: Pan, Licheng, et al.
Published: (2026)

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)

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning
by: Li, Mengyang
Published: (2025)

Distribution-Aware Robust Learning from Long-Tailed Data with Noisy Labels
by: Baik, Jae Soon, et al.
Published: (2024)

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

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

Conflict-Aware Pseudo Labeling via Optimal Transport for Entity Alignment
by: Ding, Qijie, et al.
Published: (2022)

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

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

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

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

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

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

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)

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

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

Optimal Transport for Structure Learning Under Missing Data
by: Vo, Vy, et al.
Published: (2024)

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

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

APRIL: Active Partial Rollouts in Reinforcement Learning to Tame Long-tail Generation
by: Zhou, Yuzhen, et al.
Published: (2025)

Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization
by: Persiianov, Mikhail, et al.
Published: (2024)

APLOT: Robust Reward Modeling via Adaptive Preference Learning with Optimal Transport
by: Li, Zhuo, et al.
Published: (2025)

FedEFC: Federated Learning Using Enhanced Forward Correction Against Noisy Labels
by: Yu, Seunghun, 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)

Cross-Domain Imitation Learning via Optimal Transport
by: Fickinger, Arnaud, et al.
Published: (2021)

Align Your Intents: Offline Imitation Learning via Optimal Transport
by: Bobrin, Maksim, et al.
Published: (2024)

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

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

Score-Based Diffusion Policy Compatible with Reinforcement Learning via Optimal Transport
by: Sun, Mingyang, et al.
Published: (2025)

Counterfactual Identifiability via Dynamic Optimal Transport
by: Ribeiro, Fabio De Sousa, 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)

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

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

Is Optimal Transport Necessary for Inverse Reinforcement Learning?
by: Dong, Zixuan, et al.
Published: (2025)

Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection
by: Liu, Huafeng, et al.
Published: (2024)

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

CANDERE-COACH: Reinforcement Learning from Noisy Feedback
by: Li, Yuxuan, et al.
Published: (2024)