Learning Active Learning from Data
This work addresses the challenge of selecting informative samples in active learning for machine learning practitioners, offering a novel method that improves upon existing heuristics.
The paper tackles the problem of active learning by proposing a data-driven approach that learns query selection strategies from previous outcomes, achieving effective performance across various real-world domains.
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.