Amortized nonmyopic active search via deep imitation learning
<|reference_start|>Amortized nonmyopic active search via deep imitation learning: Active search formalizes a specialized active learning setting where the goal is to collect members of a rare, valuable class. The state-of-the-art algorithm approximates the optimal Bayesian policy in a budget-aware manner, and has been shown to achieve impressive empirical performance in previous work. However, even this approximate policy has a superlinear computational complexity with respect to the size of the search problem, rendering its application impractical in large spaces or in real-time systems where decisions must be made quickly. We study the amortization of this policy by training a neural network to learn to search. To circumvent the difficulty of learning from scratch, we appeal to imitation learning techniques to mimic the behavior of the expert, expensive-to-compute policy. Our policy network, trained on synthetic data, learns a beneficial search strategy that yields nonmyopic decisions carefully balancing exploration and exploitation. Extensive experiments demonstrate our policy achieves competitive performance at real-world tasks that closely approximates the expert's at a fraction of the cost, while outperforming cheaper baselines.<|reference_end|>
@article{nguyen2024amortized,
    title={Amortized nonmyopic active search via deep imitation learning},
    author={Quan Nguyen, Anindya Sarkar, Roman Garnett},
    journal={arXiv preprint arXiv:2405.15031},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15031},
    primaryClass={cs.LG}
}