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

2026-03-30

AI Summary

This work addresses the challenge of rapidly detecting unknown change points under false alarm constraints in multi-channel settings where only a single stream can be observed at each time step. The study introduces, for the first time, an upper confidence bound (UCB) mechanism into quickest change-point detection under controlled sensing, proposing an adaptive sampling strategy that prioritizes observing the channel with the highest information gain while respecting the prescribed false alarm rate. The method accommodates general scenarios where mean shifts may occur in any subset of channels and enjoys both asymptotic optimality and computational efficiency. Experimental results demonstrate that the proposed algorithm significantly outperforms existing approaches on synthetic data, achieving substantially lower detection delay and reduced computational cost while strictly adhering to the false alarm constraint.

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.

Problem

• quickest change detection
• controlled sensing
• bandit feedback
• multichannel
• false alarm control

Innovation

• UCB-driven controlled sensing
• quickest change detection
• multi-armed bandits
• asymptotic optimality
• piecewise stationary environments