The paper introduces novel algorithms for online continuous adversarial DR-submodular optimization, addressing monotone and non-monotone functions, different feedback types, and convex feasible regions. The proposed algorithms outperform existing methods in terms of regret bounds and query complexities. By utilizing meta-actions, random permutations, and a smoothing trick, the algorithms achieve efficient optimization with fewer queries. Experimental results demonstrate superior performance compared to baseline methods.
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by Mohammad Ped... a las arxiv.org 03-18-2024
https://arxiv.org/pdf/2403.10063.pdfConsultas más profundas