Learning What to Defer for Maximum Independent Sets

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dc.contributor.authorAhn, Sungsooko
dc.contributor.authorSEO, YOUNGGYOko
dc.contributor.authorShin, Jinwooko
dc.date.accessioned2020-12-15T05:50:31Z-
dc.date.available2020-12-15T05:50:31Z-
dc.date.created2020-12-02-
dc.date.created2020-12-02-
dc.date.issued2020-07-15-
dc.identifier.citationThirty-seventh International Conference on Machine Learning, ICML 2020, pp.122 - 132-
dc.identifier.issn2640-3498-
dc.identifier.urihttp://hdl.handle.net/10203/278489-
dc.description.abstractDesigning efficient algorithms for combinatorial optimization appears ubiquitously in various scientific fields. Recently, deep reinforcement learning (DRL) frameworks have gained considerable attention as a new approach: they can automate the design of a solver while relying less on sophisticated domain knowledge of the target problem. However, the existing DRL solvers determine the solution using a number of stages proportional to the number of elements in the solution, which severely limits their applicability to large-scale graphs. In this paper, we seek to resolve this issue by proposing a novel DRL scheme, coined learning what to defer (LwD), where the agent adaptively shrinks or stretch the number of stages by learning to distribute the element-wise decisions of the solution at each stage. We apply the proposed framework to the maximum independent set (MIS) problem, and demonstrate its significant improvement over the current state-of-the-art DRL scheme. We also show that LwD can outperform the conventional MIS solvers on large-scale graphs having millions of vertices, under a limited time budget.-
dc.languageEnglish-
dc.publisherInternational Conference on Machine Learning-
dc.titleLearning What to Defer for Maximum Independent Sets-
dc.typeConference-
dc.identifier.wosid000683178500014-
dc.identifier.scopusid2-s2.0-85105139000-
dc.type.rimsCONF-
dc.citation.beginningpage122-
dc.citation.endingpage132-
dc.citation.publicationnameThirty-seventh International Conference on Machine Learning, ICML 2020-
dc.identifier.conferencecountryAU-
dc.identifier.conferencelocationVirtual-
dc.contributor.localauthorShin, Jinwoo-
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