Cover Image

Uncertainty Herding: One Active Learning Method for All Label Budgets

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bae, Wonho, Oliveira, Gabriel L., Sutherland, Danica J.
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

Generalized Coverage for More Robust Low-Budget Active Learning
by: Bae, Wonho, et al.
Published: (2024)

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

Diffusion-Driven Two-Stage Active Learning for Low-Budget Semantic Segmentation
by: Kim, Jeongin, et al.
Published: (2025)

Understanding Simplicity Bias towards Compositional Mappings via Learning Dynamics
by: Ren, Yi, et al.
Published: (2024)

Learning Dynamics of LLM Finetuning
by: Ren, Yi, et al.
Published: (2024)

Learning Representations for Independence Testing
by: Xu, Nathaniel, et al.
Published: (2024)

Random Is Hard to Beat: Active Selection in online DPO with Modern LLMs
by: Oh, Giyeong, et al.
Published: (2026)

Efficient kernelized bandit algorithms via exploration distributions
by: Hu, Bingshan, et al.
Published: (2025)

Uncertainty Quantification for Machine Learning: One Size Does Not Fit All
by: Hofman, Paul, et al.
Published: (2025)

Maximum Mean Discrepancy with Unequal Sample Sizes via Generalized U-Statistics
by: Wei, Aaron, et al.
Published: (2025)

Geometry of Uncertainty: Learning Metric Spaces for Multimodal State Estimation in RL
by: Reichlin, Alfredo, et al.
Published: (2026)

Vicinity-Guided Discriminative Latent Diffusion for Privacy-Preserving Domain Adaptation
by: Wang, Jing, et al.
Published: (2025)

Direct Acquisition Optimization for Low-Budget Active Learning
by: Zhao, Zhuokai, et al.
Published: (2024)

Why Do You Grok? A Theoretical Analysis of Grokking Modular Addition
by: Mohamadi, Mohamad Amin, et al.
Published: (2024)

A Theory for Compressibility of Graph Transformers for Transductive Learning
by: Shirzad, Hamed, et al.
Published: (2024)

Budgeted Online Active Learning with Expert Advice and Episodic Priors
by: Goebel, Kristen, et al.
Published: (2025)

Practical Kernel Tests of Conditional Independence
by: Pogodin, Roman, et al.
Published: (2024)

SeedER: Seed-and-Expand Retrieval from Knowledge Graphs
by: Shirzad, Hamed, et al.
Published: (2026)

Uncertainty for Active Learning on Graphs
by: Fuchsgruber, Dominik, et al.
Published: (2024)

Incremental Uncertainty-aware Performance Monitoring with Active Labeling Intervention
by: Koebler, Alexander, et al.
Published: (2025)

Bias Amplification in Language Model Evolution: An Iterated Learning Perspective
by: Ren, Yi, et al.
Published: (2024)

Multi-Label Bayesian Active Learning with Inter-Label Relationships
by: Qi, Yuanyuan, et al.
Published: (2024)

HGC-Herd: Efficient Heterogeneous Graph Condensation via Representative Node Herding
by: Ou, Fuyan, et al.
Published: (2025)

DUAL: Learning Diverse Kernels for Aggregated Two-sample and Independence Testing
by: Zhou, Zhijian, et al.
Published: (2025)

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

All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining
by: Zhao, Haihong, et al.
Published: (2024)

Calibrated Uncertainty Sampling for Active Learning
by: Bui, Ha Manh, et al.
Published: (2025)

DCoM: Active Learning for All Learners
by: Mishal, Inbal, et al.
Published: (2024)

A Novel Active Learning Approach to Label One Million Unknown Malware Variants
by: Bensaoud, Ahmed, et al.
Published: (2025)

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

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)

Budget-constrained Active Learning to Effectively De-censor Survival Data
by: Parsaee, Ali, et al.
Published: (2025)

Token Hidden Reward: Steering Exploration-Exploitation in Group Relative Deep Reinforcement Learning
by: Deng, Wenlong, et al.
Published: (2025)

Even Sparser Graph Transformers
by: Shirzad, Hamed, et al.
Published: (2024)

UAKNN: Label Distribution Learning via Uncertainty-Aware KNN
by: Wang, Pu, et al.
Published: (2025)

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

On the Hardness of Conditional Independence Testing In Practice
by: He, Zheng, 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)

Personalization on a Budget: Minimally-Labeled Continual Learning for Resource-Efficient Seizure Detection
by: Shahbazinia, Amirhossein, et al.
Published: (2025)