Bandit Guided Submodular Curriculum for Adaptive Subset Selection
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918221839335424 |
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| author | Chanda, Prateek Agrawal, Prayas Sureka, Saral Polu, Lokesh Reddy Kshirsagar, Atharv Ramakrishnan, Ganesh |
| author_facet | Chanda, Prateek Agrawal, Prayas Sureka, Saral Polu, Lokesh Reddy Kshirsagar, Atharv Ramakrishnan, Ganesh |
| contents | 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 validationdriven reward metrics offer a principled way to guide the curriculum schedule. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_22944 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bandit Guided Submodular Curriculum for Adaptive Subset Selection Chanda, Prateek Agrawal, Prayas Sureka, Saral Polu, Lokesh Reddy Kshirsagar, Atharv Ramakrishnan, Ganesh Machine Learning Artificial Intelligence 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 validationdriven reward metrics offer a principled way to guide the curriculum schedule. |
| title | Bandit Guided Submodular Curriculum for Adaptive Subset Selection |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2511.22944 |