A general framework and algorithm for constrained contextual online decision-making with regret bounds expressed in terms of a generalized eluder dimension and an offline density estimation oracle.
On Refined Versions of the Azuma-Hoeffding Inequality with Applications in Information Theory
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abstract
This is a survey paper with some original results of the author on refined versions of the Azuma-Hoeffding inequality with some examples that are related to information theory. This work has evolved to the joint paper with Maxim Raginsky in arXiv:1212.4663v3.
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Constrained Online Decision-Making: A Unified Framework
A general framework and algorithm for constrained contextual online decision-making with regret bounds expressed in terms of a generalized eluder dimension and an offline density estimation oracle.