Efficient Linear Bandits via Cluster-Aware Sketching

2026-09-23

AI Summary

本文针对高维度线性bandits问题，提出了一种基于聚类的CS-LB算法，通过保留每个簇内的完整协方差信息并调整草图大小来减少计算成本，同时保证了较低的遗憾。

Abstract

We study the problem of computational efficiency for linear bandits in high-dimensional settings with a finite arm set. In linear bandits, the increase in the dimension $d$ of the feature vectors leads to growing computational costs of $O(d^2)$ at each round of update. Traditional sketching-based methods such as SOFUL reduce computation via fixed-size matrix sketching, yet run the risk of incurring vacuous linear regret when the spectral tail of the data is heavy and the sketch size is inadequately selected. To guarantee regret convergence and effectively reduce computational costs, we introduce a clustering mechanism and propose the Cluster Sketch Linear Bandit (CS-LB) algorithm. Our method preserves the full covariance information in each cluster to guarantee robust sublinear regret without spectral-tail vulnerabilities, performs cluster switching by assigning a sentinel for each cluster, and reduces per-round update computation to $O(l^2d)$ via a tunable sketch size $l<d$. Experiments on synthetic datasets demonstrate that our method consistently maintains a favorable trade-off between efficiency and regret.

Problem

• Linear Bandits
• High-dimensional Settings
• Computational Efficiency
• Spectral Tail

Innovation

• Cluster-Aware Sketching
• Sublinear Regret
• Computational Efficiency