研究人员正在通过几篇探讨该问题不同方面的新论文来推进线性上下文老虎机领域。一项研究侧重于高维设置，提出了一种在线稀疏估计算法，以实现对特征维度的对数遗憾依赖。另一篇论文介绍了带付费观测的老虎机算法，实现了具有竞争力的对抗性和随机遗憾率。此外，一种新方法改进了具有梯度变化的老虎机凸优化的维度依赖性，另一项研究探讨了混合如何简化具有马尔可夫上下文的线性老虎机。 AI
影响 这些论文为老虎机算法做出了理论贡献，有可能改善复杂、数据稀疏环境中的决策。
排序理由 集群包含多篇 arXiv 上的学术论文，详细介绍了机器学习算法的理论进展。
AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →
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arXiv:2311.01327v3 Announce Type: replace Abstract: We investigate the contextual bandits with knapsack (CBwK) problem in a high-dimensional linear setting, where the feature dimension can be very large. Our goal is to harness sparsity to obtain sharper regret guarantees. To this…
arXiv:2510.07424v3 Announce Type: replace Abstract: We study linear contextual bandits with paid observations, where at each round the learner observes a context, selects an action, and may pay a fixed cost to observe feedback from a subset of arms. We propose two Follow-the-Regu…
arXiv:2602.04761v2 Announce Type: replace Abstract: Gradient-variation online learning has drawn increasing attention due to its deep connections to game theory and optimization. It has been studied extensively in the full-information setting, but is underexplored with bandit fee…
arXiv:2603.12530v3 Announce Type: replace Abstract: Recent work shows that when contexts are drawn i.i.d., linear contextual bandits can be reduced to single-context linear bandits. This ``contexts are cheap'' perspective is highly advantageous, as it allows for sharper finite-ti…