New Algorithm for Multi-Armed Bandits with Dynamically Arriving Options
Researchers have developed UCB-AA, an algorithm for multi-armed bandit problems where new options become available over time during sequential experimentation. The algorithm addresses challenges like arrival information discrepancy and drifting benchmarks by using elimination-based procedures with preliminary screening. This work is relevant for real-world applications like clinical trials and online experimentation where new treatments or actions emerge during ongoing studies.
A new machine learning algorithm called UCB-AA (UCB for Arriving Arms) has been proposed to handle multi-armed bandit problems in which the set of available arms expands dynamically over time. Traditional bandit algorithms evaluate performance against a single best arm in hindsight, but this approach is inappropriate when new options arrive during an experiment. The researchers instead introduce a dynamic-regret criterion that compares performance against the best arm currently available at any given time. UCB-AA uses an elimination-based procedure with a preliminary screening step for newly arrived arms before they compete with established arms. The algorithm achieves sublinear dynamic regret under certain conditions and can be extended to handle unknown time horizons. Simulation results demonstrate that UCB-AA reduces wasted experimental pulls and maintains a smaller set of active arms while preserving competitive regret performance.
What's missing
The study does not discuss computational complexity or scalability of the UCB-AA algorithm, nor does it provide empirical validation on real-world datasets beyond simulations. The paper also does not compare performance against alternative approaches for handling arriving-arm scenarios.
What different sources said
- arXiv cs.LGCenter
Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits
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