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Online Estimation via Offline Estimation: An Information-Theoretic Framework
April 17, 2024, 4:42 a.m. | Dylan J. Foster, Yanjun Han, Jian Qian, Alexander Rakhlin
cs.LG updates on arXiv.org arxiv.org
Abstract: $ $The classical theory of statistical estimation aims to estimate a parameter of interest under data generated from a fixed design ("offline estimation"), while the contemporary theory of online learning provides algorithms for estimation under adaptively chosen covariates ("online estimation"). Motivated by connections between estimation and interactive decision making, we ask: is it possible to convert offline estimation algorithms into online estimation algorithms in a black-box fashion? We investigate this question from an information-theoretic perspective …
abstract algorithms arxiv cs.lg data design framework generated information interactive math.st offline online learning statistical stat.ml stat.th theory type via
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