aiqbenchInformații despre AI & calcul cuantic

...

Română · beta. Traducerea a fost făcută automat și este încă în curs de revizuire. Citește pagina în engleză

perspectiva aiqbenchMachine 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.

Sursă care acoperăarXiv

Ce este aceastaLectura unui model asupra articolului și a acoperirii conexe, scrisă pentru aiqbench. Nu sunt cuvintele publicației.

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

arXiv16 iunie 20261 min de cititArticolul și perspectiva sunt în engleză.

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)

Text integral sub licența deschisă a publicației · 102 cuvinte

Citește pe arXiv ↗Întreabă despre acest articol