Web: http://arxiv.org/abs/2206.11183

June 23, 2022, 1:12 a.m. | Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson

stat.ML updates on arXiv.org arxiv.org

Active learning methods have shown great promise in reducing the number of
samples necessary for learning. As automated learning systems are adopted into
real-time, real-world decision-making pipelines, it is increasingly important
that such algorithms are designed with safety in mind. In this work we
investigate the complexity of learning the best safe decision in interactive
environments. We reduce this problem to a constrained linear bandits problem,
where our goal is to find the best arm satisfying certain (unknown) safety
constraints. …

active learning arxiv constraints learning lg safety

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