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cs.IT2025
Finite-Horizon Quickest Change Detection Balancing Latency with False Alarm Probability
Yu-Han Huang, Venugopal V. Veeravalli
A finite-horizon variant of the quickest change detection (QCD) problem that is of relevance to learning in non-stationary environments is studied. The metric characterizing false…
cs.IT2025
Sequential Change Detection for Learning in Piecewise Stationary Bandit Environments
Yu-Han Huang, Venugopal V. Veeravalli
A finite-horizon variant of the quickest change detection problem is investigated, which is motivated by a change detection problem that arises in piecewise stationary bandits. The…
cs.AI2025
Detection Augmented Bandit Procedures for Piecewise Stationary MABs: A Modular Approach
Yu-Han Huang, Argyrios Gerogiannis, Subhonmesh Bose +1
Conventional Multi-Armed Bandit (MAB) algorithms are designed for stationary environments, where the reward distributions associated with the arms do not change with time. In many…