English(EN) Eluder-Dimension-Free Regret for Stage-Wise Constrained Contextual Bandits via Offline Regression

新算法通过离线回归解决约束上下文老虎机问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了一种名为 CS-UCCB 的新算法，用于处理具有阶段性约束的上下文老虎机。该算法旨在平衡风险控制与从结果中学习，尤其是在仅观察到所选动作的奖励和成本时。CS-UCCB 结合了离线回归、成本置信集和上反事实置信界，在特定的熵和误差条件下，实现了每轮的可行性以及无逃逸维度项的次线性遗憾。 AI

影响为机器学习中受约束的决策引入了一种新颖的算法方法。

排序理由详细介绍机器学习特定问题新算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 4 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍机器学习特定问题新算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Haichen Hu, David Simchi-Levi, Navid Azizan · 2026-10-23 04:00

Eluder-Dimension-Free Regret for Stage-Wise Constrained Contextual Bandits via Offline Regression

arXiv:2505.07101v4 Announce Type: replace-cross Abstract: Contextual bandits with stage-wise constraints model decisions that must control risk while learning from their outcomes. We study this problem when only the selected action's reward and cost are observed, with a general r…

本文如何被排名

报道来源 [1]

Eluder-Dimension-Free Regret for Stage-Wise Constrained Contextual Bandits via Offline Regression

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