Learning Active Learning from Data

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 real data from a wide range of domains.

Published in:
Advances in Neural Information Processing Systems 30 (NIPS 2017)
Presented at:
Conference on Neural Information Processing Systems (NIPS)

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 Record created 2018-01-24, last modified 2020-10-28

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