Online learning for min-max discrete problems

12 Jul 2019Evripidis BampisDimitris ChristouBruno EscoffierNguyen Kim Thang

We study various discrete nonlinear combinatorial optimization problems in an online learning framework. In the first part, we address the question of whether there are negative results showing that getting a vanishing (or even vanishing approximate) regret is computational hard... (read more)

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