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Online Learning with Unknown Constraints
March 8, 2024, 5:41 a.m. | Karthik Sridharan, Seung Won Wilson Yoo
cs.LG updates on arXiv.org arxiv.org
Abstract: We consider the problem of online learning where the sequence of actions played by the learner must adhere to an unknown safety constraint at every round. The goal is to minimize regret with respect to the best safe action in hindsight while simultaneously satisfying the safety constraint with high probability on each round. We provide a general meta-algorithm that leverages an online regression oracle to estimate the unknown safety constraint, and converts the predictions of …
abstract arxiv constraints cs.ai cs.lg every math.st online learning probability safety stat.ml stat.th type
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