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Found 5 papers, 1 papers with code
The Label Complexity of Active Learning from Observational Data
We provably demonstrate that the result of this is an algorithm which is statistically consistent as well as more label-efficient than prior work.
Exploring Connections Between Active Learning and Model Extraction
This has resulted in the surge of Machine Learning-as-a-Service (MLaaS) - cloud services that provide (a) tools and resources to learn the model, and (b) a user-friendly query interface to access the model.
Active Learning with Logged Data
We consider active learning with logged data, where labeled examples are drawn conditioned on a predetermined logging policy, and the goal is to learn a classifier on the entire population, not just conditioned on the logging policy.
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces
It has been a long-standing problem to efficiently learn a halfspace using as few labels as possible in the presence of noise.
Active Learning from Imperfect Labelers
We study active learning where the labeler can not only return incorrect labels but also abstain from labeling.
