Fully automatic deep learning has become the state-of-the-art technique for many tasks
including image acquisition, analysis and interpretation, and for the extraction of clinically …

This article surveys recent work in active learning aimed at making it more practical for real-
world use. In general, active learning systems aim to make machine learning more …

The key idea behind active learning is that a machine learning algorithm can achieve
greater accuracy with fewer labeled training instances if it is allowed to choose the training …

I draw the reader's attention to machine teaching, the problem of finding an optimal training
set given a machine learning algorithm and a target model. In addition to generating …

The problem of inducing, learning or inferring grammars has been studied for decades, but
only in recent years has grammatical inference emerged as an independent field with …

We propose a new active learning by query synthesis approach using Generative
Adversarial Networks (GAN). Different from regular active learning, the resulting algorithm …

We state and analyze the first active learning algorithm which works in the presence of
arbitrary forms of noise. The algorithm, A 2 (for Agnostic Active), relies only upon the …

An active learner has a collection of data points, each with a label that is initially hidden but
can be obtained at some cost. Without spending too much, it wishes to find a classifier that …

We abstract out the core search problem of active learning schemes, to better understand
the extent to which adaptive labeling can improve sam-ple complexity. We give various …

Coarse sample complexity bounds for active learning Page 1 Coarse sample complexity
bounds for active learning Sanjoy Dasgupta UC San Diego dasgupta@cs.ucsd.edu Abstract …