The Complexity of Sequential Prediction in Dynamical Systems
CoRR(2024)
摘要
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.
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