Learning Where to Look: UCB-Driven Controlled Sensing for Quickest Change Detection

TL;DR

This paper introduces UCB-inspired methods for multichannel quickest change detection with controlled sensing, achieving asymptotic optimality and outperforming existing approaches in synthetic benchmarks.

Contribution

The paper proposes novel, computationally efficient UCB-based detection procedures for controlled sensing in change detection, including unknown distribution settings.

Findings

Achieves first-order asymptotic optimality in detection delay.

Outperforms existing state-of-the-art methods in simulations.

Offers substantial computational savings.

Abstract

We study the multichannel quickest change detection problem with bandit feedback and controlled sensing, in which an agent sequentially selects one of the data streams to observe at each time-step and aims to detect an unknown change as quickly as possible while controlling false alarms. Assuming known pre- and post-change distributions and allowing an arbitrary subset of streams to be affected by the change, we propose two novel and computationally efficient detection procedures inspired by the Upper Confidence Bound (UCB) multi-armed bandit algorithm. Our methods adaptively concentrate sensing on the most informative streams while preserving false-alarm guarantees. We show that both procedures achieve first-order asymptotic optimality in detection delay under standard false-alarm constraints. We also extend the UCB-driven controlled sensing approach to the setting where the pre- and post-change distributions are unknown, except for a mean-shift in at least one of the channels at the change-point. This setting is particularly relevant to the problem of learning in piecewise stationary environments. Finally, extensive simulations on synthetic benchmarks show that our methods consistently outperform existing state-of-the-art approaches while offering substantial computational savings.