English(EN) BAGEL: Adversarially Constrained Online Convex Optimization under Separation Oracle Access

新的BAGEL算法解决了具有分离预言机访问的对抗COCO问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-27 04:00

研究人员开发了一种名为BAGEL的新算法，用于对抗约束在线凸优化（COCO）。该算法旨在即使在只能通过分离预言机（SO）访问动作集的情况下也能表现良好，而不是更强大的预言机，如投影预言机（PO）或线性优化预言机（LOO）。BAGEL在低遗憾和低累积约束违反之间实现了理论上的平衡，使用的SO调用次数接近线性。 AI

影响为具有有限预言机访问的优化问题引入了新的理论框架，可能影响机器学习中的算法设计。

排序理由该集群包含一篇详细介绍特定优化问题新算法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yiyang Lu, Mohammad Pedramfar, Mengbo Wang, Vaneet Aggarwal · 2026-08-27 04:00

BAGEL：分离预言机访问下的对抗性约束在线凸优化

arXiv:2502.16744v3 Announce Type: replace Abstract: In adversarial Constrained Online Convex Optimization (COCO), a learner selects actions from a fixed convex set while seeking both low regret and low cumulative constraint violation (CCV) under time-varying constraints. We ask w…

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BAGEL：分离预言机访问下的对抗性约束在线凸优化

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