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.
To Another Language
from source content
arxiv.org
Önemli Bilgiler Şuradan Elde Edildi
by Mohammad Ped... : arxiv.org 03-18-2024
https://arxiv.org/pdf/2403.10063.pdfDaha Derin Sorular