Learning How to Actively Learn: A Deep Imitation Learning Approach

Code

• github.com/Grayming/ALIL
  OfficialIn papertf★ 0

Abstract

Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary. We introduce a method that learns an AL ``policy'' using ``imitation learning'' (IL). Our IL-based approach makes use of an efficient and effective ``algorithmic expert'', which provides the policy learner with good actions in the encountered AL situations. The AL strategy is then learned with a feedforward network, mapping situations to most informative query datapoints. We evaluate our method on two different tasks: text classification and named entity recognition. Experimental results show that our IL-based AL strategy is more effective than strong previous methods using heuristics and reinforcement learning.

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