Copertina

Learning Causal Transition Matrix for Instance-dependent Label Noise

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Li, Jiahui, Chang, Tai-Wei, Kuang, Kun, Li, Ximing, Chen, Long, Zhou, Jun
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

Forming Auxiliary High-confident Instance-level Loss to Promote Learning from Label Proportions
di: Ma, Tianhao, et al.
Pubblicazione: (2024)

Learning from Label Proportions with Dual-proportion Constraints
di: Ma, Tianhao, et al.
Pubblicazione: (2026)

Instance Relation Learning Network with Label Knowledge Propagation for Few-shot Multi-label Intent Detection
di: Zhao, Shiman, et al.
Pubblicazione: (2025)

Causal Temporal Representation Learning with Nonstationary Sparse Transition
di: Song, Xiangchen, et al.
Pubblicazione: (2024)

Reduction-based Pseudo-label Generation for Instance-dependent Partial Label Learning
di: Qiao, Congyu, et al.
Pubblicazione: (2024)

Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning
di: Tang, Wei, et al.
Pubblicazione: (2024)

On the Role of Label Noise in the Feature Learning Process
di: Han, Andi, et al.
Pubblicazione: (2025)

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

Multi-Instance Partial-Label Learning with Margin Adjustment
di: Tang, Wei, et al.
Pubblicazione: (2025)

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning
di: Tang, Wei, et al.
Pubblicazione: (2025)

Best Transition Matrix Esitimation or Best Label Noise Robustness Classifier? Two Possible Methods to Enhance the Performance of T-revision
di: Liu, Haixu, et al.
Pubblicazione: (2025)

Efficient Ensemble Conditional Independence Test Framework for Causal Discovery
di: Guan, Zhengkang, et al.
Pubblicazione: (2025)

Instance-dependent Convergence Theory for Diffusion Models
di: Jiao, Yuchen, et al.
Pubblicazione: (2024)

Representational Transfer Learning for Matrix Completion
di: He, Yong, et al.
Pubblicazione: (2024)

Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewards
di: Hu, Zijing, et al.
Pubblicazione: (2025)

Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization
di: Zhang, Kuan, et al.
Pubblicazione: (2025)

Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling
di: Zhu, Dingyuan, et al.
Pubblicazione: (2024)

Learning from Label Proportions and Covariate-shifted Instances
di: Singh, Sagalpreet, et al.
Pubblicazione: (2024)

Mitigating Instance-Dependent Label Noise: Integrating Self-Supervised Pretraining with Pseudo-Label Refinement
di: Bala, Gouranga, et al.
Pubblicazione: (2024)

Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLM-Powered Assistance
di: Yuan, Bo, et al.
Pubblicazione: (2025)

Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark
di: Kim, Suyeon, et al.
Pubblicazione: (2025)

Tuning the Right Foundation Models is What you Need for Partial Label Learning
di: He, Kuang, et al.
Pubblicazione: (2025)

Mitigating Instance Entanglement in Instance-Dependent Partial Label Learning
di: Zhao, Rui, et al.
Pubblicazione: (2026)

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

Learning from Stochastic Labels
di: Wei, Meng, et al.
Pubblicazione: (2023)

Causal Discovery for Irregularly Time Series with Consistency Guarantees
di: Li, Weihong, et al.
Pubblicazione: (2025)

Instance-dependent Early Stopping
di: Yuan, Suqin, et al.
Pubblicazione: (2025)

On the Learning Dynamics of Two-layer Linear Networks with Label Noise SGD
di: Zhang, Tongcheng, et al.
Pubblicazione: (2026)

Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning
di: Bae, HeeSun, et al.
Pubblicazione: (2024)

Inaccurate Label Distribution Learning with Dependency Noise
di: Kou, Zhiqiang, et al.
Pubblicazione: (2024)

Dual-Label Learning With Irregularly Present Labels
di: Li, Mingqian, et al.
Pubblicazione: (2024)

Instance-Aware Graph Prompt Learning
di: Li, Jiazheng, et al.
Pubblicazione: (2024)

Causal Inference with Complex Treatments: A Survey
di: Wang, Yingrong, et al.
Pubblicazione: (2024)

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

Federated Learning with Instance-Dependent Noisy Label
di: Wang, Lei, et al.
Pubblicazione: (2023)

CausalVerse: Benchmarking Causal Representation Learning with Configurable High-Fidelity Simulations
di: Chen, Guangyi, et al.
Pubblicazione: (2025)

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

Class-Imbalanced Complementary-Label Learning via Weighted Loss
di: Wei, Meng, et al.
Pubblicazione: (2022)

Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation
di: Liu, Haoyang, et al.
Pubblicazione: (2023)