aiqbench takeMachine Learning

This article presents Detection Augmented Learning (DAL), a novel framework designed to handle piecewise stationary bandits without requiring prior knowledge of non-stationarity. By integrating change detection with existing optimal regret algorithms, DAL demonstrates superior performance across synthetic benchmarks and real-world datasets compared to current state-of-the-art methods. The work offers significant theoretical insights into adapting standard machine learning agents for dynamic environments where underlying distributions shift over time.

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DAL: A Practical Prior-Free Black-Box Framework for Piecewise Stationary Bandits

arXivJune 16, 20261 min read

arXiv:2501.19401v5 Announce Type: replace Abstract: We introduce a practical, black-box framework termed Detection Augmented Learning (DAL) for the problem of piecewise stationary bandits without knowledge of the underlying non-stationarity. DAL accepts any stationary bandit algorithm with order-optimal regret as input and augments it with a change detector, enabling applicability to all common bandit variants. Extensive experimentation demonstrates that DAL consistently surpasses all state-of-the-art methods across diverse non-stationary scenarios, including synthetic benchmarks and real-world datasets, underscoring its versatility and scalability. We provide theoretical insights into DAL's strong empirical performance, complemented by thorough empirical validation.

Abstract:We introduce a practical, black-box framework termed Detection Augmented Learning (DAL) for the problem of piecewise stationary bandits without knowledge of the underlying non-stationarity. DAL accepts any stationary bandit algorithm with order-optimal regret as input and augments it with a change detector, enabling applicability to all common bandit variants. Extensive experimentation demonstrates that DAL consistently surpasses all state-of-the-art methods across diverse non-stationary scenarios, including synthetic benchmarks and real-world datasets, underscoring its versatility and scalability. We provide theoretical insights into DAL's strong empirical performance, complemented by thorough empirical validation.

Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2501.19401 [cs.LG]

(or arXiv:2501.19401v5 [cs.LG] for this version)

Full text under the publisher's open licence · 102 words

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