No-regret algorithms for online $k$-submodular maximization

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Tasuku Soma ;
Proceedings of Machine Learning Research, PMLR 89:1205-1214, 2019.

Abstract

We present a polynomial time algorithm for online maximization of $k$-submodular maximization. For online (nonmonotone) $k$-submodular maximization, our algorithm achieves a tight approximate factor in the approximate regret. For online monotone $k$-submodular maximization, our approximate-regret matches to the best-known approximation ratio, which is tight asymptotically as $k$ tends to infinity. Our approach is based on the Blackwell approachability theorem and online linear optimization.

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