Bandit Guided Submodular Curriculum for Adaptive Subset Selection

NeurIPS 2025 Conference Site

Prateek Chanda, Prayas Agrawal, Saral Sureka, Lokesh Reddy Polu, Atharv Kshirsagar, Ganesh Ramakrishnan
Department of Computer Science, IIT Bombay
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Abstract

Traditional curriculum learning proceeds from easy to hard samples, yet defining a reliable notion of difficulty remains elusive. Prior work has used submodular functions to induce difficulty scores in curriculum learning. We reinterpret adaptive subset selection and formulate it as a multi-armed bandit problem, where each arm corresponds to a submodular function guiding sample selection. We introduce OnlineSubmod, a novel online greedy policy that optimizes a utility-driven reward and provably achieves no-regret performance under various sampling regimes. Empirically, OnlineSubmod outperforms both traditional curriculum learning and bi-level optimization approaches across vision and language datasets, showing superior accuracy-efficiency tradeoffs. More broadly, we show that validation-driven reward metrics offer a principled way to guide the curriculum schedule. Our code is publicly available at GitHub : https://github.com/efficiency-learning/banditsubmod/.

BibTeX

@inproceedings{chanda2025bandit,
  title={Bandit Guided Submodular Curriculum for Adaptive Subset Selection},
  author={Chanda, Prateek and Agrawal, Prayas and Sureka, Saral and Polu, Lokesh Reddy and Kshirsagar, Atharv and Ramakrishnan, Ganesh},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2025}
}