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In this paper, we suggest a novel data-driven approach to active learning
(AL). The key idea is to train a regressor that predicts the expected error
reduction for a candidate sample in a particular learning state. By
formulating the query selection procedure as a regression problem we are not
restricted to working with existing AL heuristics; instead, we learn
strategies based on experience from previous AL outcomes. We show that a
strategy can be learnt either from simple synthetic 2D datasets or from a
subset of domain-specific data. Our method yields strategies that work well on