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English(EN) Dynamic Regret in Online Convex Optimization with Indicator Switching Costs

新研究以新方法解决在线学习中的动态遗憾问题 · 跟踪3个来源

作者 PulseAugur 编辑部 · [3 个来源] ·

三篇新研究论文探讨了在线学习中的动态遗憾最小化问题。第一篇论文侧重于针对弯曲损失和Adam等自适应优化器的FTRL（Follow-the-Regularized-Leader）方法，提出了一种简化证明并提供新保证的约简。第二篇论文引入了一个用于具有指示器切换成本的动态遗憾的元学习框架，实现了跟踪分段常数比较器的最小最大最优界。第三篇论文分析了上下文线性优化中遗憾的曲率，推导了一个闭式度量，并展示了其在电池套利任务中将遗憾提高30.8%的应用。 AI

影响 这些论文推进了对在线学习算法的理论理解，可能导致在动态环境中更高效、更自适应的AI系统。

排序理由 该集群包含三篇关于理论机器学习主题的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

新研究以新方法解决在线学习中的动态遗憾问题 · 跟踪3个来源

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该集群包含三篇关于理论机器学习主题的学术论文。
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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Yan-Feng Xie, Yu-Jie Zhang, Peng Zhao, Zhi-Hua Zhou ·

    通过折扣到动态的约简实现动态遗憾，并应用于曲线损失和Adam优化器

    arXiv:2602.08372v2 Announce Type: replace Abstract: We study dynamic regret minimization in non-stationary online learning, with a primary focus on follow-the-regularized-leader (FTRL) methods. FTRL is important for curved losses and for understanding adaptive optimizers such as …

  2. arXiv cs.LG TIER_1 English(EN) · Naram Mhaisen, George Iosifidis ·

    带指示器切换成本的在线凸优化中的动态遗憾

    arXiv:2609.30556v1 Announce Type: new Abstract: We study dynamic regret in online convex optimization with an \emph{indicator switching cost}: a fixed penalty incurred whenever two consecutive decisions differ. This captures startup overheads such as server activation, model depl…

  3. arXiv stat.ML TIER_1 English(EN) · Konstantinos Ziliaskopoulos, Alexander Vinel, Alice E. Smith ·

    上下文线性优化中的遗憾曲率

    arXiv:2610.01980v1 Announce Type: cross Abstract: Decision-focused learning for linear optimization is complicated by the discontinuity of the optimizer, where small cost errors may leave the decision unchanged or move it to a different vertex. We show that this non-smooth pointw…

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