Papers › Discovering General-Purpose Active Learning Strategies

Discovering General-Purpose Active Learning Strategies

9 Oct 2018ICLR 2019 5arXiv:1810.04114archive 2025-07-28

Ksenia Konyushkova, Raphael Sznitman, Pascal Fua

We propose a general-purpose approach to discovering active learning (AL) strategies from data. These strategies are transferable from one domain to another and can be used in conjunction with many machine learning models. To this end, we formalize the annotation process as a Markov decision process, design universal state and action spaces and introduce a new reward function that precisely model the AL objective of minimizing the annotation cost. We seek to find an optimal (non-myopic) AL strategy using reinforcement learning. We evaluate the learned strategies on multiple unrelated domains and show that they consistently outperform state-of-the-art baselines.

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Active Learning BIG-bench Machine Learning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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