Bootstrap-Conditioned Action Selection with Tabular Foundation Models
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Computer Science > Machine Learning
Title:Bootstrap-Conditioned Action Selection with Tabular Foundation Models
Abstract:Contextual bandits offer a natural framework for sample-efficient personalization, but practical deployment remains difficult under sparse, biased interaction data, unreliable uncertainty estimates, and severe cold starts. We study whether pre-trained tabular foundation models with in-context learning can be turned into randomized policies for online decision making. We propose BC-ICL (Bootstrap-conditioned action selection using ICL), which at each round draws a bootstrap resample of the interaction history, conditions a frozen pre-trained ICL model on that resample, scores all actions, and selects the action with the highest sampled score. We further introduce an arm-context conditioning architecture that promotes shared statistical strength across actions and helps avoid common bootstrap failure modes of isolated-arm bandits. Empirically, this policy delivers strong early-round regret and regret performance on standard contextual bandit suites, outperforming established baselines under a strict online protocol.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.06559 [cs.LG] |
| (or arXiv:2608.06559v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.06559
arXiv-issued DOI via DataCite (pending registration)
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