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Poster
Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback
Shinji Ito · Daisuke Hatano · Hanna Sumita · Kei Takemura · Takuro Fukunaga · Naonori Kakimura · Ken-Ichi Kawarabayashi

Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #20
in Algorithms -- Bandit Algorithms »
We propose computationally efficient algorithms for \textit{online linear optimization with bandit feedback}, in which a player chooses an \textit{action vector} from a given (possibly infinite) set $\mathcal{A} \subseteq \mathbb{R}^d$, and then suffers a loss that can be expressed as a linear function in action vectors. Although existing algorithms achieve an optimal regret bound of $\tilde{O}(\sqrt{T})$ for $T$ rounds (ignoring factors of $\mathrm{poly} (d, \log T)$), computationally efficient ways of implementing them have not yet been specified, in particular when $|\mathcal{A}|$ is not bounded by a polynomial size in $d$. A standard way to pursue computational efficiency is to assume that we have an efficient algorithm referred to as \textit{oracle} that solves (offline) linear optimization problems over $\mathcal{A}$. Under this assumption, the computational efficiency of a bandit algorithm can then be measured in terms of \textit{oracle complexity}, i.e., the number of oracle calls. Our contribution is to propose algorithms that offer optimal regret bounds of $\tilde{O}(\sqrt{T})$ as well as low oracle complexity for both \textit{non-stochastic settings} and \textit{stochastic settings}. Our algorithm for non-stochastic settings has an oracle complexity of $\tilde{O}( T )$ and is the first algorithm that achieves both a regret bound of $\tilde{O}( \sqrt{T} )$ and an oracle complexity of $\tilde{O} ( \mathrm{poly} ( T ) )$, given only linear optimization oracles. Our algorithm for stochastic settings calls the oracle only $O( \mathrm{poly} (d, \log T))$ times, which is smaller than the current best oracle complexity of $O( T )$ if $T$ is sufficiently large.
Keywords: Bandit Algorithms · Algorithms · Algorithms; Algorithms -> Online Learning; Optimization -> Combinatorial Optimization; Optimization · Convex Optimization; The
[ Paper]  [ Poster] 

Author Information

Shinji Ito (NEC Corporation)
Daisuke Hatano (RIKEN AIP)
Hanna Sumita (Tokyo Metropolitan University)
Kei Takemura (NEC Corporation)
Takuro Fukunaga (Chuo University, JST PRESTO, RIKEN AIP)
Naonori Kakimura (Keio University)
Ken-Ichi Kawarabayashi (National Institute of Informatics)

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