Generalized Coverage for More Robust Low-Budget Active Learning
<|reference_start|>Generalized Coverage for More Robust Low-Budget Active Learning: The ProbCover method of Yehuda et al. is a well-motivated algorithm for active learning in low-budget regimes, which attempts to "cover" the data distribution with balls of a given radius at selected data points. We demonstrate, however, that the performance of this algorithm is extremely sensitive to the choice of this radius hyper-parameter, and that tuning it is quite difficult, with the original heuristic frequently failing. We thus introduce (and theoretically motivate) a generalized notion of "coverage," including ProbCover's objective as a special case, but also allowing smoother notions that are far more robust to hyper-parameter choice. We propose an efficient greedy method to optimize this coverage, generalizing ProbCover's algorithm; due to its close connection to kernel herding, we call it "MaxHerding." The objective can also be optimized non-greedily through a variant of $k$-medoids, clarifying the relationship to other low-budget active learning methods. In comprehensive experiments, MaxHerding surpasses existing active learning methods across multiple low-budget image classification benchmarks, and does so with less computational cost than most competitive methods.<|reference_end|>
@article{bae2024generalized,
    title={Generalized Coverage for More Robust Low-Budget Active Learning},
    author={Wonho Bae, Junhyug Noh, Danica J. Sutherland},
    journal={arXiv preprint arXiv:2407.12212},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12212},
    primaryClass={cs.LG}
}