Learning How to Actively Learn: A Deep Imitation Learning Approach
Ming Liu, Wray Buntine, Gholamreza Haffari · 2018
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.