新理论在在线凸优化中实现对数高概率遗憾

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-13 04:00

研究人员为在线凸优化（OCO）开发了一个新的理论框架，实现了对数高概率遗憾。这一进展解决了在每步只有两次函数评估的有限反馈下进行学习的挑战。所提出的方法在先前的分析基础上有了显著改进，特别是在维度依赖性方面，与早期工作的二次项相比，其线性依赖性得以保持，同时保持了对迭代次数的对数依赖性。 AI

影响在线凸优化领域的这一理论进展可能有助于开发在有限反馈下运行的AI系统中更高效的学习算法。

排序理由该集群包含一篇在arXiv上发表的研究论文，详细介绍了在线凸优化的理论进展。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Haishan Ye · 2026-07-13 04:00

具有两点老虎机反馈的在线凸优化对数高概率遗憾

arXiv:2603.25029v4 Announce Type: replace Abstract: We study online convex optimization (OCO) with two-point bandit feedback against a non-anticipating adaptive adversary. In this setting, a learner competes with an adversarial sequence of convex losses while observing each loss …