# Generalized Coverage for More Robust Low-Budget Active Learning Source: [Generalized Coverage for More Robust Low-Budget Active Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:06:43.001Z Authors: Wonho Bae, Junhyug Noh, Danica J. Sutherland Published: 2024-07-16T23:21:51.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Active Learning for Cascaded Table Extraction on business-int](): Active Learning for Cascaded Table Extraction; business-int; AUC: 56.71. - [Active Learning for Cascaded Table Extraction on Cross-dataset Aggregate](): Active Learning for Cascaded Table Extraction; Cross-dataset Aggregate; Mean AUC: 65.02. - [Active Learning for Cascaded Table Extraction on FinTabNet](): Active Learning for Cascaded Table Extraction; FinTabNet; AUC: 69.88. - [Active Learning for Cascaded Table Extraction on invoice-int](): Active Learning for Cascaded Table Extraction; invoice-int; AUC: 63.8. - [Active Learning for Cascaded Table Extraction on PubTables-1M](): Active Learning for Cascaded Table Extraction; PubTables-1M; AUC: 69.7. ## Original sources and implementation [Original paper]() [PDF]()