Finite sample learning of moving targets
<|reference_start|>Finite sample learning of moving targets: 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.<|reference_end|>
@article{vertovec2024finite,
    title={Finite sample learning of moving targets},
    author={Nikolaus Vertovec, Kostas Margellos, Maria Prandini},
    journal={arXiv preprint arXiv:2408.04406},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04406},
    primaryClass={math.OC cs.LG}
}