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Expected Worst Case Regret via Stochastic Sequential Covering. (arXiv:2209.04417v1 [cs.LG])
Sept. 12, 2022, 1:11 a.m. | Changlong Wu, Mohsen Heidari, Ananth Grama, Wojciech Szpankowski
cs.LG updates on arXiv.org arxiv.org
We study the problem of sequential prediction and online minimax regret with
stochastically generated features under a general loss function. We introduce a
notion of expected worst case minimax regret that generalizes and encompasses
prior known minimax regrets. For such minimax regrets we establish tight upper
bounds via a novel concept of stochastic global sequential covering. We show
that for a hypothesis class of VC-dimension $\mathsf{VC}$ and $i.i.d.$
generated features of length $T$, the cardinality of the stochastic global
sequential …
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