The Complexity of Sequential Prediction in Dynamical Systems
<|reference_start|>The Complexity of Sequential Prediction in Dynamical Systems: We study the problem of learning to predict the next state of a dynamical system when the underlying evolution function is unknown. Unlike previous work, we place no parametric assumptions on the dynamical system, and study the problem from a learning theory perspective. We define new combinatorial measures and dimensions and show that they quantify the optimal mistake and regret bounds in the realizable and agnostic setting respectively.<|reference_end|>
@article{raman2024the,
    title={The Complexity of Sequential Prediction in Dynamical Systems},
    author={Vinod Raman, Unique Subedi, Ambuj Tewari},
    journal={arXiv preprint arXiv:2402.06614},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06614},
    primaryClass={cs.LG stat.ML}
}