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arXiv · 2408.04406

Finite sample learning of moving targets

Nikolaus Vertovec·Kostas Margellos·Maria Prandini
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Abstract

We consider a moving target that we seek to learn from samples. Our results extend randomized techniques developed in control and optimization for a constant target to the case where the target is changing. We derive a novel bound on the number of samples that are required to construct a probably approximately correct (PAC) estimate of the target. Furthermore, when the moving target is a convex polytope, we provide a constructive method of generating the PAC estimate using a mixed integer linear program (MILP). The proposed method is demonstrated on an application to autonomous emergency braking.

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Nikolaus Vertovec, Kostas Margellos, Maria Prandini. 2024-08-08. Finite sample learning of moving targets. https://arxiv.org/abs/2408.04406

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