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- W2963088049 abstract "We revisit the problem of textit{online linear optimization} in case the set of feasible actions is accessible through an approximated linear optimization oracle with a factor $alpha$ multiplicative approximation guarantee. This setting is in particular interesting since it captures natural online extensions of well-studied textit{offline} linear optimization problems which are NP-hard, yet admit efficient approximation algorithms. The goal here is to minimize the $alpha$textit{-regret} which is the natural extension of the standard textit{regret} in textit{online learning} to this setting. We present new algorithms with significantly improved oracle complexity for both the full information and bandit variants of the problem. Mainly, for both variants, we present $alpha$-regret bounds of $O(T^{-1/3})$, were $T$ is the number of prediction rounds, using only $O(log{T})$ calls to the approximation oracle per iteration, on average. These are the first results to obtain both average oracle complexity of $O(log{T})$ (or even poly-logarithmic in $T$) and $alpha$-regret bound $O(T^{-c})$ for a constant $c>0$, for both variants." @default.
- W2963088049 created "2019-07-30" @default.
- W2963088049 creator A5019533927 @default.
- W2963088049 date "2017-09-10" @default.
- W2963088049 modified "2023-09-25" @default.
- W2963088049 title "Efficient Online Linear Optimization with Approximation Algorithms" @default.
- W2963088049 doi "https://doi.org/10.48550/arxiv.1709.03093" @default.
- W2963088049 hasPublicationYear "2017" @default.
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