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- W3210576317 abstract "A classic problem in statistics is the estimation of the expectation of random variables from samples. This gives rise to the tightly connected problems of deriving concentration inequalities and confidence sequences, that is confidence intervals that hold uniformly over time. Previous work has shown how to easily convert the regret guarantee of an online betting algorithm into a time-uniform concentration inequality. In this paper, we show that we can go even further: We show that the regret of universal portfolio algorithms give rise to new implicit time-uniform concentrations and state-of-the-art empirically calculated confidence sequences. In particular, our numerically obtained confidence sequences can never be vacuous, even with a single sample, and satisfy the law of iterated logarithm." @default.
- W3210576317 created "2021-11-08" @default.
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- W3210576317 date "2021-10-26" @default.
- W3210576317 modified "2023-09-23" @default.
- W3210576317 title "Tight Concentrations and Confidence Sequences from the Regret of Universal Portfolio" @default.
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- W3210576317 doi "https://doi.org/10.48550/arxiv.2110.14099" @default.
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