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

Tasuku Soma
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v89-soma19a, title = {No-regret algorithms for online $k$-submodular maximization}, author = {Soma, Tasuku}, booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics}, pages = {1205--1214}, year = {2019}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, volume = {89}, series = {Proceedings of Machine Learning Research}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v89/soma19a/soma19a.pdf}, url = {https://proceedings.mlr.press/v89/soma19a.html}, 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.} }
Endnote
%0 Conference Paper %T No-regret algorithms for online $k$-submodular maximization %A Tasuku Soma %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-soma19a %I PMLR %P 1205--1214 %U https://proceedings.mlr.press/v89/soma19a.html %V 89 %X 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.
APA
Soma, T.. (2019). No-regret algorithms for online $k$-submodular maximization. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:1205-1214 Available from https://proceedings.mlr.press/v89/soma19a.html.

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