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- W4226095006 abstract "Concentration inequalities are widely used for analyzing machine learning algorithms. However, the current concentration inequalities cannot be applied to some non-causal processes which appear for instance in Natural Language Processing (NLP). This is mainly due to the non-causal nature of such involved data, in the sense that each data point depends on other neighboring data points. In this paper, we establish a framework for modeling non-causal random fields and prove a Hoeffding-type concentration inequality. The proof of this result is based on a local approximation of the non-causal random field by a function of a finite number of i.i.d. random variables." @default.
- W4226095006 created "2022-05-05" @default.
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- W4226095006 date "2022-01-01" @default.
- W4226095006 modified "2023-10-14" @default.
- W4226095006 title "Concentration inequalities for non-causal random fields" @default.
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- W4226095006 doi "https://doi.org/10.1214/22-ejs1992" @default.
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