:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Qi, Yuanyuan, Lu, Jueqing, Yang, Xiaohao, Enticott, Joanne, Du, Lan
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2411.17941
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Next Generation Active Learning: Mixture of LLMs in the Loop
by: Qi, Yuanyuan, et al.
Published: (2026)

ALScope: A Unified Toolkit for Deep Active Learning
by: Wu, Chenkai, et al.
Published: (2025)

Navigating Conflicting Views: Harnessing Trust for Learning
by: Lu, Jueqing, et al.
Published: (2024)

DPL: Decoupled Prototype Learning for Enhancing Robustness of Vision-Language Transformers to Missing Modalities
by: Lu, Jueqing, et al.
Published: (2025)

Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification
by: Tan, Wei, et al.
Published: (2024)

Scalable Label Distribution Learning for Multi-Label Classification
by: Zhao, Xingyu, et al.
Published: (2023)

Batch Bayesian Active Learning with Partial Batch Label Sampling
by: Hu, Kangping, et al.
Published: (2025)

Neural Topic Modeling with Large Language Models in the Loop
by: Yang, Xiaohao, et al.
Published: (2024)

To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation
by: Azeemi, Abdul Hameed, et al.
Published: (2024)

FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning
by: Kou, Zhiqiang, et al.
Published: (2026)

ActiveLab: Active Learning with Re-Labeling by Multiple Annotators
by: Goh, Hui Wen, et al.
Published: (2023)

Label Distribution Learning with Biased Annotations by Learning Multi-Label Representation
by: Kou, Zhiqiang, et al.
Published: (2025)

Optimal Labeler Assignment and Sampling for Active Learning in the Presence of Imperfect Labels
by: Ahadi, Pouya, et al.
Published: (2025)

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

Labeled TrustSet Guided: Batch Active Learning with Reinforcement Learning
by: Cui, Guofeng, et al.
Published: (2026)

Confidence Self-Calibration for Multi-Label Class-Incremental Learning
by: Du, Kaile, et al.
Published: (2024)

Towards Generalising Neural Topical Representations
by: Yang, Xiaohao, et al.
Published: (2023)

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

Feature-Label Modal Alignment for Robust Partial Multi-Label Learning
by: Chen, Yu, et al.
Published: (2026)

Fair Active Learning: Solving the Labeling Problem in Insurance
by: Elie, Romuald, et al.
Published: (2021)

Batch Selection and Communication for Active Learning with Edge Labeling
by: Croisfelt, Victor, et al.
Published: (2023)

Label Forensics: Interpreting Hard Labels in Black-Box Text Classifier
by: Du, Mengyao, et al.
Published: (2025)

In-Context Learning Learns Label Relationships but Is Not Conventional Learning
by: Kossen, Jannik, et al.
Published: (2023)

Label Cluster Chains for Multi-Label Classification
by: Gatto, Elaine Cecília, et al.
Published: (2024)

Bayesian-guided Label Mapping for Visual Reprogramming
by: Cai, Chengyi, et al.
Published: (2024)

Federated Learning with Only Positive Labels by Exploring Label Correlations
by: An, Xuming, et al.
Published: (2024)

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

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

Pushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels
by: Li, Xiang, et al.
Published: (2023)

Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling
by: Bae, Wonho, et al.
Published: (2023)

FOCAL-Attention for Heterogeneous Multi-Label Prediction
by: Zhang, Chenghao, et al.
Published: (2026)

Minimal Learning Machine for Multi-Label Learning
by: Hämäläinen, Joonas, et al.
Published: (2023)

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

Human-Corrected Labels Learning: Enhancing Labels Quality via Human Correction of VLMs Discrepancies
by: Li, Zhongnian, et al.
Published: (2025)

Weakly Supervised Label Learning Flows
by: Lu, You, et al.
Published: (2023)

Leveraging Label Semantics and Meta-Label Refinement for Multi-Label Question Classification
by: Dong, Shi, et al.
Published: (2024)

Uncertainty Herding: One Active Learning Method for All Label Budgets
by: Bae, Wonho, et al.
Published: (2024)

Active Learning of General Halfspaces: Label Queries vs Membership Queries
by: Diakonikolas, Ilias, et al.
Published: (2024)

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

Learning from Ambiguous Data with Hard Labels
by: Xie, Zeke, et al.
Published: (2025)