Active Learning
Active learning is a machine learning paradigm focused on optimizing data labeling efficiency by strategically selecting the most informative samples for annotation from a larger unlabeled pool. Current research emphasizes developing novel acquisition functions and data pruning strategies to reduce computational costs associated with large datasets, exploring the integration of active learning with various model architectures (including deep neural networks, Gaussian processes, and language models), and addressing challenges like privacy preservation and handling open-set noise. This approach holds significant promise for reducing the substantial cost and effort of data labeling in diverse fields, ranging from image classification and natural language processing to materials science and healthcare.
Papers
MEAL: Stable and Active Learning for Few-Shot Prompting
Abdullatif Köksal, Timo Schick, Hinrich Schütze
An Efficient Active Learning Pipeline for Legal Text Classification
Sepideh Mamooler, Rémi Lebret, Stéphane Massonnet, Karl Aberer
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data
Hengrui Zhang, Wei Wayne Chen, James M. Rondinelli, Wei Chen
Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop
Md Tahmid Rahman Laskar, Cheng Chen, Xue-Yong Fu, Shashi Bhushan TN
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning
Paul E. Chang, Prakhar Verma, ST John, Victor Picheny, Henry Moss, Arno Solin
Neural Active Learning on Heteroskedastic Distributions
Savya Khosla, Chew Kin Whye, Jordan T. Ash, Cyril Zhang, Kenji Kawaguchi, Alex Lamb